International Journal of Trend in Research and Development, IJTRD, 2018
In the context of Association Rule mining for finding useful rules, a novel Interestingness Measu... more In the context of Association Rule mining for finding useful rules, a novel Interestingness Measure is presented. This measure is inspired from the method of Obtaining the Comprehensive Indicator through the Geometric Methods and discussed in this paper. As a statistics based method, association rule mining has certain limitations. First of all, the generation of the association rules is totally based on the fact, data without considering the relationship between the rules. Secondly, affected by data quality and selection of threshold, the generator may produce useless rules or even lose some useful rules. Thirdly, the expression ability of association rules is limited. Thus, evaluating the reliability of the obtained association rules becomes one of the hot spots for researchers. Study on traditional association rules mining is based on support-confidence framework, and the rules are called strong association rules only when they satisfy both thresholds of support and confidence. However, sometimes strong association rules are not what users are interested in and are even misleading. Thus need is to further analyze and evaluate the mined rules in order to find the most valuable association. Generally, since most rules with high support are obvious or are already known by users, low support rules that provide users with some interesting new knowledge may be more novel than high support rules. However, with too low support threshold, it can also produce the combination explosion problem. So the best way to resolve this dilemma is to first set a low support threshold or use dynamic support threshold to complete a series of mining and then employ the new association rules measure framework to screen mining results and extract the most valuable and interesting association rules at the same time.
IRACST-International Journal of Computer Science and Information Technology & Security (IJCSITS), 2016
field of data processing is changing as fastly as the volume is increasing at a faster pace and a... more field of data processing is changing as fastly as the volume is increasing at a faster pace and as the more intelligent and automated viewpoint for looking at data are the need of the time. This changing need is from all dimensions of life like Business, Biology Medical Research, Education, Governance, Risk Analysis, Text Analysis and Social Relation Management. These domains putting more and more difficult to manage storage, and computationally complex challenges before scientific community. To summarize or to take decisions, finding interesting and useful patterns in data is must. Association Rule Mining is the branch of data mining that is very helpful in this context. Association rule mining plays critical and important part in knowledge mining. The difficult task in is discovering hidden knowledge i.e useful rules from the large number of rules generated for reduced support. For pruning of rules or grouping the rules, many techniques are suggested such as rule structure cover methods, informative cover methods, rule clustering, etc. Another way of selecting association rules is based on interestingness measures such as support, confidence, correlation, and so on. In this paper, we study various interestingness measures and their use in selecting interesting and useful rules. These association rules are of utmost importance in implementing software systems that are based on methods and techniques of Data Science, Big Data and Data Mining. Because, the interestingness evaluation of association rule has the significance for the practical application of association rule mining technology, so it is necessary to study and improve it
International Journal of Computer Systems (IJCS ), 2016
In this paper we present novel algorithms TempCIPFP for Mining Frequent Patterns using Counting I... more In this paper we present novel algorithms TempCIPFP for Mining Frequent Patterns using Counting Inference from Probabilistic Temporal Databases and we also discussed future possibilities. We consider the problem of discovering frequent itemsets and association-rules between/among items in a large database of transactional databases acquired under uncertainty in certain time. With each timestamped transaction associated is a probability gives the confidence that the transaction occurred with given probability on that time. We discuss generalized algorithms for solving this problem that are fundamentally different from the known algorithms. Complete demonstration of algorithm presented and discussed in this paper. We will also show how the best features of the algorithm can be combined into a business system. In this paper, we address the problem of the efficiency of the main phase of most data mining applications: The frequent pattern extraction. This problem is mainly related to the number of operations required for counting pattern supports in the database, and we propose a new method, called the counting inference probabilistic frequent pattern algorithm to mine probabilistic temporal databases TempCIPFP, that allows to perform as few support counts as possible. Using this method, the support of a pattern is determined without accessing the database whenever possible, using the supports of some of its sub-patterns called key patterns. This method was implemented in the TempCIPFP, counting inference based probabilistic frequent pattern mining algorithm that is an optimization of the simple and efficient Apriori algorithm.
International Journal of Computer Applications (IJCA), 2016
We consider the problem of discovering frequent item sets and association rules between items in ... more We consider the problem of discovering frequent item sets and association rules between items in a large database of transactional databases acquired under uncertainty. A probabilistic database considered here is one in which with each transaction associated is a probability, represents the confidence that the transaction will occur with given associated certainty. In this paper, we address the problem of the efficiency of the main phase of most data mining applications: The frequent pattern extraction. This problem is mainly related to the number of operations required for counting pattern supports in the database and we propose a new method, called counting inference probabilistic frequent pattern miner in probabilistic databases, this algorithm allows to perform as few support counts as possible. It is optimized to reduce the number of database scan as well as the number of patterns for which explicit support count is required. Using this method, the support of a pattern is determined without accessing the database whenever possible, using the supports of some of its sub-patterns called key patterns. This method was implemented in the CIPFP, counting inference based probabilistic frequent pattern mining algorithm that is an optimization of the simple and efficient Apriori algorithm. The goal is to transform all key patterns into non-key patterns as early as possible as for non-key-patterns database scan is not required at all.
IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS),, 2016
In this paper we studied Finding Frequent Item Sets and Association Rules with the Apriori Algori... more In this paper we studied Finding Frequent Item Sets and Association Rules with the Apriori Algorithm. This comparative study refers to Borgelt's Apriori implementation version 6.12 in particular and original Apriori by R. Agrawal and R. Srikant. This paper covers introduction to Apriori, an Association Rule Mining algorithm and discussed related issues like Basic Notions of Items and Transactions, Support of an Item Set, Confidence of an Association Rule, Support of an Association Rule, and also discussed Target Types like Frequent Item Sets, Closed Item Sets, Maximal Item Sets, Generators/Free Item Sets, Association Rules, and Extended Rule Selection and their variations, for confidence, lift, conviction, χ 2-Measure, p-value, and importance of Fisher's Exact test for table probability, χ 2-Measure, information gain, support, and in the end item set selection methods are discussed. All these measures are very important to invent new data mining algorithms and they all have very strong utilization and application in data science and business intelligence. This paper also discuss The possible application of association rule mining from the perspective of Business Intelligence and Data Science. This review is important as this implementation is useful for Business Analysts, Data Scientists and data miners who want to exploit association rules to take decisions using R programming language to find solution fo business problems.
International Journal of Computer Systems (IJCA), 2016
Traditional association rules mining cannot meet the demands arising from some real applications.... more Traditional association rules mining cannot meet the demands arising from some real applications. Such domains are like the mining of sensor data, geographical system data, biological data. These domain suffer from uncertain data. To capture inherent uncertainty probabilistic databases were introduced. In contrast to the traditional data mining algorithm, by considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. In this paper, we presented n extension to Two-Phase algorithm that is extended to mine probabilistic high utility patterns, to efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets, transaction weighted utility, high utility, counting inference, and probability threshold are used. It performs very efficiently in terms of speed and memory cost both on synthetic and real databases, even on large databases that are difficult for existing algorithms to handle when the algorithm is run using counting inference when it is applicable.
International Research Journal of Engineering and Technology(IRJET), 2016
we consider the problem of discovering frequent item sets and association rules between items in ... more we consider the problem of discovering frequent item sets and association rules between items in a large database of transactional databases acquired under uncertainty. With each transaction associated is a probability gives the confidence that the transaction will occur. We discuss generalized algorithms for solving this problem that are fundamentally different from the known algorithms. Complete demonstration of algorithm presented and discussed in this paper. We also show how the best features of the algorithm can be combined into a business system.
The previous bit-search technique does not provide any compaction or compression mechanism the de... more The previous bit-search technique does not provide any compaction or compression mechanism the density in bitvector regions. As a result, on the sparse dataset only one or two bits are set in each bit-search region, which not only increase the projection length but also it is not possible to achieve true 32bit CPU performance. To increase the density in bit-vector regions the Bit Search Mask Search starts with an array list. From root node, a bit search mask search representation for each frequent item is created which gives a sufficient compression and compaction in bit search. In this paper we can find sufficient improvements in Bit search Mask Search by using this approach.
─In general, a database system will not operate properly if it exist some null values of attribut... more ─In general, a database system will not operate properly if it exist some null values of attributes in the system. Traditional clustering methods were developed to analyze complete data sets. Faults during the data collection, data transfer or data cleaning often lead to missing values in data so that common clustering methods cannot be used for the data analysis. In our approach, two datasets has been taken from abalone (UCI repository) and preprocessing is performed. Thus, this work basically focuses on the merits of using evolutionary algorithm for data clustering that contains missing values and clearly compares it to the existing K-Means clustering method. Through this approach, the accuracy of estimating null values is improved; also the error rate is minimized.
In this fast and furious era ubiquitous computing is growing so extensively and every manual work... more In this fast and furious era ubiquitous computing is growing so extensively and every manual works are shifting towards computerized work. People don’t want to invest their time in processing they just want to give an input and take an output immediately. And by the help of machines we can reduce the processing time. The OMR sheet can be scanned by any simple scanner and by the use of this System it can be easily assessed also the result can be put into the database of the system. The Software is designed to serve that entire organization who conducts their exam on OMR sheet. Different organizations could use to assess and evaluate their answer sheet on various scales and Parameters. The system could help in many ways and could turn out to be a revelation for the Organizations and also for the HR people.
The web is a huge repository of knowledge and numerous hyperlinks. Web also serves a broad divers... more The web is a huge repository of knowledge and numerous hyperlinks. Web also serves a broad diversity of user communities and global information service centers. Every day the knowledge in web page upwards rapidly. Web pages can be used to convey the knowledge to web users. Such voluminous size of the web makes an intricacy of web information retrieval, web content filtering and web structure mining. Hence, it is essential for proper categorization of web pages. This paper demonstrates the web page categorization problem as the multi classification task and provides a suitable solution using a supervised learning technique namely multilayer perceptron. The classification model is generated by learning the features that have been extracted from HTML structure and URL of the web page. Feature reduction techniques have been applied to select optimum features and a model is learned. The experimental results of the multilayer perceptron models before and after feature reduction has been e...
The image depends on the Human perception and is also based on the Machine Vision System. The Ima... more The image depends on the Human perception and is also based on the Machine Vision System. The Image Retrieval is based on the color Histogram, texture. The perception of the Human System of Image is based on the Human Neurons which hold the 10 12 of Information; the Human brain continuously learns with the sensory organs like eye which transmits the Image to the brain which interprets the Image. The research challenge is that how the brain processes the information in the semantic manner is hot area of research till date. Hence we can say that a single set of Algorithm cannot define the complete set of operations on the field of Image Mining. This Paper examines the State-of - art technology-Image mining techniques which is based on the Color Histogram, texture of him Image. The query Image is taken then the Color Histogram and Texture is taken and based on this the resultant Image is output.
For the purpose of content-based image retrieval (CBIR) An up-to-date comparison of state-of-the-... more For the purpose of content-based image retrieval (CBIR) An up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is presented in this paper. The CBIR problem is identified by us because there is a need to search the huge databases having images efficiently and effectively. in this paper we suggest a color and texture feature extraction algorithms. Special attention is given for CBIR is the similarity measurement using correlation coefficient with distinct distance matrices properties. A New approach for image retrieval technique is proposed to improve retrieval performance, and reduce the extraction search times. Matching is performed between the test image and the object image and quality of matching is measured in terms of grading.
International Journal of Advanced Research in Computer Science and Electronics Engineering, Dec 28, 2012
Software security failures are common and a long standing challenge to the research community. We... more Software security failures are common and a long standing challenge to the research community. We can conceptualize the vulnerability of an application through its attack surface size. A system's attack surface is an indicator of the system’s security. Unfortunately predicting software’s future attack surface size during design phase in earlier stage of software development life cycle (SDLC) is largely missing. Our objective is to investigate the statistical relationship between system’s attack surface with various size and complexity metrics to find a set of size and complexity metrics which is/are best suitable to predict software’s future attack surface early in software development cycle. In this research paper, we investigate whether software design metrics can be utilized as early indicators of system’s future attack surface size. For an experimental setting, nine open-source java-based projects were analyzed. Our experimental results indicate that architectural information from the non-security realm such as design metrics are useful in system’s attack surface size prediction.
— With the accessing of millions of web pages for business and personal transactions, huge amount... more — With the accessing of millions of web pages for business and personal transactions, huge amounts of web page access data have been stored in web servers. The existing web pattern mining systems do not provide the necessary tools and functionality to capture all stored distributed web page sequences easily. Data mining represents the process of extracting interesting and previously unknown knowledge from data. Through the approach described in this paper we address the important data mining problem of sequential web pattern access. We present an approach for mining the sequential web patterns with Distributed mining model. Here the environment under consideration is the one where raw input data is collected from number of web servers at distributed locations. This approach is a combination of distributed concept for web personalization. It utilizes the concept of WAP algorithm and provides ease of getting web sequential patterns enhancing " Web Service Resource Framework ". I. INTRODUCTION Data mining refers to extracting or " mining " knowledge from large amounts of data. Data mining should have been more appropriately named knowledge mining from data. There are many other terms carrying a similar or slightly different meaning to data mining, such as knowledge mining from databases, knowledge extraction, data/pattern analysis, data archaeology, and data dredging [13]. One of the techniques of data mining is Web Mining which is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the Worldwide Web. There are roughly three knowledge discovery domains that pertain to Web mining as Web Content Mining, Web Structure Mining, and Web Usage Mining. Web content mining is the process of extracting knowledge from the content of documents or their descriptions. Web document text mining, resource discovery based on concepts indexing or agent based technology may also fall in this category. Web structure mining is the process of inferring knowledge from the Worldwide Web organization and links between references and referents in the Web. Finally, web usage mining, also known as Web Log
2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009
Abstract An up-to-date comparison of state-of-the-art low-level color and texture feature extract... more Abstract An up-to-date comparison of state-of-the-art low-level color and texture feature extraction methods, for the purpose of content-based image retrieval (CBIR) is presented in this paper. The CBIR problem is motivated by the need to search the exponentially ...
... [17] Alexander Wöhrer, Eduard Mehofer and Peter Brezany, 2010 Sixth IEEE International Confer... more ... [17] Alexander Wöhrer, Eduard Mehofer and Peter Brezany, 2010 Sixth IEEE International Conference on eScience Workshops. [18] David Chiu, Apeksha Shetty and Gagan Agrawal, SC10 November 2010, New Orleans, Louisiana,IEEE. Page 6. ...
Traffic modeling and classification find importance in many areas such as bandwidth management, t... more Traffic modeling and classification find importance in many areas such as bandwidth management, traffic analysis, prediction and engineering, network planning, Quality of Service provisioning and anomalous traffic detection. Much of research work has been done in the area of network traffic classification by application type and several classifiers are suggested. In past network traffic classification using traditional techniques such as well known port number based and payload analysis based techniques are no more effective because various applications uses port hopping and encryption technique to avoid detection. Recently machine learning techniques such as supervised, unsupervised and semi supervised techniques are used to overcome the problems of traditional techniques. In this work we use semi supervised machine learning approach to classify the network traffic using DBSCAN algorithm. This techniques uses only flow statistics to classify the network traffic. This methodology is...
In this paper we have dealt with the problem of finding frequent itemsets which can be used to ma... more In this paper we have dealt with the problem of finding frequent itemsets which can be used to make strong association rules. Although a number of methods have been developed, those can mine frequent item sets. Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes.Emerging applications introduce the requirement for novel associationrule mining algorithms that will be scalable not only with respect to the number of records (number of rows) but also with respect to the domain’s size (number of columns). In this paper, searching algorithms are closely related to the concept of dictionaries. String searching algorithms are too complex in all sorts of applications. For analyzing an algorithm to determining the amount of resources like time and storage it is necessary to execute it. To work with input of arbitrary le...
2012 Fourth International Conference on Computational Intelligence and Communication Networks, 2012
We are in an age repeatedly referred to the information age. Information is gathered almost every... more We are in an age repeatedly referred to the information age. Information is gathered almost everywhere in our everyday lives. The amount of information stored in modern databases makes manual analysis intractable. Data mining provides tools to reveal previously unknown information in large databases. A number of algorithms have been proposed to determine frequent pattern. Apriori algorithm is the first algorithm proposed in this field. With the time a number of changes proposed in Apriori to enhance the performance in term of time and number of database passes. A well-known data mining technique is association rule mining. Association rule mining is a well-known technique in data mining. It is able to reveal all interesting relationships, called associations, in a potentially large database. However, how interesting a rule is depends on the problem a user wants to solve.This paper describes new technique cluster based Master-Slave structural. It uses Hybrid technique, the grouping of bottom up and top down technique for search repetitive item sets. It reduces the time in use to get out the support count of the item sets. The Prime number show present offers the give for verify rules and provides decrease in the data complexity.
International Journal of Trend in Research and Development, IJTRD, 2018
In the context of Association Rule mining for finding useful rules, a novel Interestingness Measu... more In the context of Association Rule mining for finding useful rules, a novel Interestingness Measure is presented. This measure is inspired from the method of Obtaining the Comprehensive Indicator through the Geometric Methods and discussed in this paper. As a statistics based method, association rule mining has certain limitations. First of all, the generation of the association rules is totally based on the fact, data without considering the relationship between the rules. Secondly, affected by data quality and selection of threshold, the generator may produce useless rules or even lose some useful rules. Thirdly, the expression ability of association rules is limited. Thus, evaluating the reliability of the obtained association rules becomes one of the hot spots for researchers. Study on traditional association rules mining is based on support-confidence framework, and the rules are called strong association rules only when they satisfy both thresholds of support and confidence. However, sometimes strong association rules are not what users are interested in and are even misleading. Thus need is to further analyze and evaluate the mined rules in order to find the most valuable association. Generally, since most rules with high support are obvious or are already known by users, low support rules that provide users with some interesting new knowledge may be more novel than high support rules. However, with too low support threshold, it can also produce the combination explosion problem. So the best way to resolve this dilemma is to first set a low support threshold or use dynamic support threshold to complete a series of mining and then employ the new association rules measure framework to screen mining results and extract the most valuable and interesting association rules at the same time.
IRACST-International Journal of Computer Science and Information Technology & Security (IJCSITS), 2016
field of data processing is changing as fastly as the volume is increasing at a faster pace and a... more field of data processing is changing as fastly as the volume is increasing at a faster pace and as the more intelligent and automated viewpoint for looking at data are the need of the time. This changing need is from all dimensions of life like Business, Biology Medical Research, Education, Governance, Risk Analysis, Text Analysis and Social Relation Management. These domains putting more and more difficult to manage storage, and computationally complex challenges before scientific community. To summarize or to take decisions, finding interesting and useful patterns in data is must. Association Rule Mining is the branch of data mining that is very helpful in this context. Association rule mining plays critical and important part in knowledge mining. The difficult task in is discovering hidden knowledge i.e useful rules from the large number of rules generated for reduced support. For pruning of rules or grouping the rules, many techniques are suggested such as rule structure cover methods, informative cover methods, rule clustering, etc. Another way of selecting association rules is based on interestingness measures such as support, confidence, correlation, and so on. In this paper, we study various interestingness measures and their use in selecting interesting and useful rules. These association rules are of utmost importance in implementing software systems that are based on methods and techniques of Data Science, Big Data and Data Mining. Because, the interestingness evaluation of association rule has the significance for the practical application of association rule mining technology, so it is necessary to study and improve it
International Journal of Computer Systems (IJCS ), 2016
In this paper we present novel algorithms TempCIPFP for Mining Frequent Patterns using Counting I... more In this paper we present novel algorithms TempCIPFP for Mining Frequent Patterns using Counting Inference from Probabilistic Temporal Databases and we also discussed future possibilities. We consider the problem of discovering frequent itemsets and association-rules between/among items in a large database of transactional databases acquired under uncertainty in certain time. With each timestamped transaction associated is a probability gives the confidence that the transaction occurred with given probability on that time. We discuss generalized algorithms for solving this problem that are fundamentally different from the known algorithms. Complete demonstration of algorithm presented and discussed in this paper. We will also show how the best features of the algorithm can be combined into a business system. In this paper, we address the problem of the efficiency of the main phase of most data mining applications: The frequent pattern extraction. This problem is mainly related to the number of operations required for counting pattern supports in the database, and we propose a new method, called the counting inference probabilistic frequent pattern algorithm to mine probabilistic temporal databases TempCIPFP, that allows to perform as few support counts as possible. Using this method, the support of a pattern is determined without accessing the database whenever possible, using the supports of some of its sub-patterns called key patterns. This method was implemented in the TempCIPFP, counting inference based probabilistic frequent pattern mining algorithm that is an optimization of the simple and efficient Apriori algorithm.
International Journal of Computer Applications (IJCA), 2016
We consider the problem of discovering frequent item sets and association rules between items in ... more We consider the problem of discovering frequent item sets and association rules between items in a large database of transactional databases acquired under uncertainty. A probabilistic database considered here is one in which with each transaction associated is a probability, represents the confidence that the transaction will occur with given associated certainty. In this paper, we address the problem of the efficiency of the main phase of most data mining applications: The frequent pattern extraction. This problem is mainly related to the number of operations required for counting pattern supports in the database and we propose a new method, called counting inference probabilistic frequent pattern miner in probabilistic databases, this algorithm allows to perform as few support counts as possible. It is optimized to reduce the number of database scan as well as the number of patterns for which explicit support count is required. Using this method, the support of a pattern is determined without accessing the database whenever possible, using the supports of some of its sub-patterns called key patterns. This method was implemented in the CIPFP, counting inference based probabilistic frequent pattern mining algorithm that is an optimization of the simple and efficient Apriori algorithm. The goal is to transform all key patterns into non-key patterns as early as possible as for non-key-patterns database scan is not required at all.
IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS),, 2016
In this paper we studied Finding Frequent Item Sets and Association Rules with the Apriori Algori... more In this paper we studied Finding Frequent Item Sets and Association Rules with the Apriori Algorithm. This comparative study refers to Borgelt's Apriori implementation version 6.12 in particular and original Apriori by R. Agrawal and R. Srikant. This paper covers introduction to Apriori, an Association Rule Mining algorithm and discussed related issues like Basic Notions of Items and Transactions, Support of an Item Set, Confidence of an Association Rule, Support of an Association Rule, and also discussed Target Types like Frequent Item Sets, Closed Item Sets, Maximal Item Sets, Generators/Free Item Sets, Association Rules, and Extended Rule Selection and their variations, for confidence, lift, conviction, χ 2-Measure, p-value, and importance of Fisher's Exact test for table probability, χ 2-Measure, information gain, support, and in the end item set selection methods are discussed. All these measures are very important to invent new data mining algorithms and they all have very strong utilization and application in data science and business intelligence. This paper also discuss The possible application of association rule mining from the perspective of Business Intelligence and Data Science. This review is important as this implementation is useful for Business Analysts, Data Scientists and data miners who want to exploit association rules to take decisions using R programming language to find solution fo business problems.
International Journal of Computer Systems (IJCA), 2016
Traditional association rules mining cannot meet the demands arising from some real applications.... more Traditional association rules mining cannot meet the demands arising from some real applications. Such domains are like the mining of sensor data, geographical system data, biological data. These domain suffer from uncertain data. To capture inherent uncertainty probabilistic databases were introduced. In contrast to the traditional data mining algorithm, by considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. In this paper, we presented n extension to Two-Phase algorithm that is extended to mine probabilistic high utility patterns, to efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets, transaction weighted utility, high utility, counting inference, and probability threshold are used. It performs very efficiently in terms of speed and memory cost both on synthetic and real databases, even on large databases that are difficult for existing algorithms to handle when the algorithm is run using counting inference when it is applicable.
International Research Journal of Engineering and Technology(IRJET), 2016
we consider the problem of discovering frequent item sets and association rules between items in ... more we consider the problem of discovering frequent item sets and association rules between items in a large database of transactional databases acquired under uncertainty. With each transaction associated is a probability gives the confidence that the transaction will occur. We discuss generalized algorithms for solving this problem that are fundamentally different from the known algorithms. Complete demonstration of algorithm presented and discussed in this paper. We also show how the best features of the algorithm can be combined into a business system.
The previous bit-search technique does not provide any compaction or compression mechanism the de... more The previous bit-search technique does not provide any compaction or compression mechanism the density in bitvector regions. As a result, on the sparse dataset only one or two bits are set in each bit-search region, which not only increase the projection length but also it is not possible to achieve true 32bit CPU performance. To increase the density in bit-vector regions the Bit Search Mask Search starts with an array list. From root node, a bit search mask search representation for each frequent item is created which gives a sufficient compression and compaction in bit search. In this paper we can find sufficient improvements in Bit search Mask Search by using this approach.
─In general, a database system will not operate properly if it exist some null values of attribut... more ─In general, a database system will not operate properly if it exist some null values of attributes in the system. Traditional clustering methods were developed to analyze complete data sets. Faults during the data collection, data transfer or data cleaning often lead to missing values in data so that common clustering methods cannot be used for the data analysis. In our approach, two datasets has been taken from abalone (UCI repository) and preprocessing is performed. Thus, this work basically focuses on the merits of using evolutionary algorithm for data clustering that contains missing values and clearly compares it to the existing K-Means clustering method. Through this approach, the accuracy of estimating null values is improved; also the error rate is minimized.
In this fast and furious era ubiquitous computing is growing so extensively and every manual work... more In this fast and furious era ubiquitous computing is growing so extensively and every manual works are shifting towards computerized work. People don’t want to invest their time in processing they just want to give an input and take an output immediately. And by the help of machines we can reduce the processing time. The OMR sheet can be scanned by any simple scanner and by the use of this System it can be easily assessed also the result can be put into the database of the system. The Software is designed to serve that entire organization who conducts their exam on OMR sheet. Different organizations could use to assess and evaluate their answer sheet on various scales and Parameters. The system could help in many ways and could turn out to be a revelation for the Organizations and also for the HR people.
The web is a huge repository of knowledge and numerous hyperlinks. Web also serves a broad divers... more The web is a huge repository of knowledge and numerous hyperlinks. Web also serves a broad diversity of user communities and global information service centers. Every day the knowledge in web page upwards rapidly. Web pages can be used to convey the knowledge to web users. Such voluminous size of the web makes an intricacy of web information retrieval, web content filtering and web structure mining. Hence, it is essential for proper categorization of web pages. This paper demonstrates the web page categorization problem as the multi classification task and provides a suitable solution using a supervised learning technique namely multilayer perceptron. The classification model is generated by learning the features that have been extracted from HTML structure and URL of the web page. Feature reduction techniques have been applied to select optimum features and a model is learned. The experimental results of the multilayer perceptron models before and after feature reduction has been e...
The image depends on the Human perception and is also based on the Machine Vision System. The Ima... more The image depends on the Human perception and is also based on the Machine Vision System. The Image Retrieval is based on the color Histogram, texture. The perception of the Human System of Image is based on the Human Neurons which hold the 10 12 of Information; the Human brain continuously learns with the sensory organs like eye which transmits the Image to the brain which interprets the Image. The research challenge is that how the brain processes the information in the semantic manner is hot area of research till date. Hence we can say that a single set of Algorithm cannot define the complete set of operations on the field of Image Mining. This Paper examines the State-of - art technology-Image mining techniques which is based on the Color Histogram, texture of him Image. The query Image is taken then the Color Histogram and Texture is taken and based on this the resultant Image is output.
For the purpose of content-based image retrieval (CBIR) An up-to-date comparison of state-of-the-... more For the purpose of content-based image retrieval (CBIR) An up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is presented in this paper. The CBIR problem is identified by us because there is a need to search the huge databases having images efficiently and effectively. in this paper we suggest a color and texture feature extraction algorithms. Special attention is given for CBIR is the similarity measurement using correlation coefficient with distinct distance matrices properties. A New approach for image retrieval technique is proposed to improve retrieval performance, and reduce the extraction search times. Matching is performed between the test image and the object image and quality of matching is measured in terms of grading.
International Journal of Advanced Research in Computer Science and Electronics Engineering, Dec 28, 2012
Software security failures are common and a long standing challenge to the research community. We... more Software security failures are common and a long standing challenge to the research community. We can conceptualize the vulnerability of an application through its attack surface size. A system's attack surface is an indicator of the system’s security. Unfortunately predicting software’s future attack surface size during design phase in earlier stage of software development life cycle (SDLC) is largely missing. Our objective is to investigate the statistical relationship between system’s attack surface with various size and complexity metrics to find a set of size and complexity metrics which is/are best suitable to predict software’s future attack surface early in software development cycle. In this research paper, we investigate whether software design metrics can be utilized as early indicators of system’s future attack surface size. For an experimental setting, nine open-source java-based projects were analyzed. Our experimental results indicate that architectural information from the non-security realm such as design metrics are useful in system’s attack surface size prediction.
— With the accessing of millions of web pages for business and personal transactions, huge amount... more — With the accessing of millions of web pages for business and personal transactions, huge amounts of web page access data have been stored in web servers. The existing web pattern mining systems do not provide the necessary tools and functionality to capture all stored distributed web page sequences easily. Data mining represents the process of extracting interesting and previously unknown knowledge from data. Through the approach described in this paper we address the important data mining problem of sequential web pattern access. We present an approach for mining the sequential web patterns with Distributed mining model. Here the environment under consideration is the one where raw input data is collected from number of web servers at distributed locations. This approach is a combination of distributed concept for web personalization. It utilizes the concept of WAP algorithm and provides ease of getting web sequential patterns enhancing " Web Service Resource Framework ". I. INTRODUCTION Data mining refers to extracting or " mining " knowledge from large amounts of data. Data mining should have been more appropriately named knowledge mining from data. There are many other terms carrying a similar or slightly different meaning to data mining, such as knowledge mining from databases, knowledge extraction, data/pattern analysis, data archaeology, and data dredging [13]. One of the techniques of data mining is Web Mining which is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the Worldwide Web. There are roughly three knowledge discovery domains that pertain to Web mining as Web Content Mining, Web Structure Mining, and Web Usage Mining. Web content mining is the process of extracting knowledge from the content of documents or their descriptions. Web document text mining, resource discovery based on concepts indexing or agent based technology may also fall in this category. Web structure mining is the process of inferring knowledge from the Worldwide Web organization and links between references and referents in the Web. Finally, web usage mining, also known as Web Log
2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009
Abstract An up-to-date comparison of state-of-the-art low-level color and texture feature extract... more Abstract An up-to-date comparison of state-of-the-art low-level color and texture feature extraction methods, for the purpose of content-based image retrieval (CBIR) is presented in this paper. The CBIR problem is motivated by the need to search the exponentially ...
... [17] Alexander Wöhrer, Eduard Mehofer and Peter Brezany, 2010 Sixth IEEE International Confer... more ... [17] Alexander Wöhrer, Eduard Mehofer and Peter Brezany, 2010 Sixth IEEE International Conference on eScience Workshops. [18] David Chiu, Apeksha Shetty and Gagan Agrawal, SC10 November 2010, New Orleans, Louisiana,IEEE. Page 6. ...
Traffic modeling and classification find importance in many areas such as bandwidth management, t... more Traffic modeling and classification find importance in many areas such as bandwidth management, traffic analysis, prediction and engineering, network planning, Quality of Service provisioning and anomalous traffic detection. Much of research work has been done in the area of network traffic classification by application type and several classifiers are suggested. In past network traffic classification using traditional techniques such as well known port number based and payload analysis based techniques are no more effective because various applications uses port hopping and encryption technique to avoid detection. Recently machine learning techniques such as supervised, unsupervised and semi supervised techniques are used to overcome the problems of traditional techniques. In this work we use semi supervised machine learning approach to classify the network traffic using DBSCAN algorithm. This techniques uses only flow statistics to classify the network traffic. This methodology is...
In this paper we have dealt with the problem of finding frequent itemsets which can be used to ma... more In this paper we have dealt with the problem of finding frequent itemsets which can be used to make strong association rules. Although a number of methods have been developed, those can mine frequent item sets. Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes.Emerging applications introduce the requirement for novel associationrule mining algorithms that will be scalable not only with respect to the number of records (number of rows) but also with respect to the domain’s size (number of columns). In this paper, searching algorithms are closely related to the concept of dictionaries. String searching algorithms are too complex in all sorts of applications. For analyzing an algorithm to determining the amount of resources like time and storage it is necessary to execute it. To work with input of arbitrary le...
2012 Fourth International Conference on Computational Intelligence and Communication Networks, 2012
We are in an age repeatedly referred to the information age. Information is gathered almost every... more We are in an age repeatedly referred to the information age. Information is gathered almost everywhere in our everyday lives. The amount of information stored in modern databases makes manual analysis intractable. Data mining provides tools to reveal previously unknown information in large databases. A number of algorithms have been proposed to determine frequent pattern. Apriori algorithm is the first algorithm proposed in this field. With the time a number of changes proposed in Apriori to enhance the performance in term of time and number of database passes. A well-known data mining technique is association rule mining. Association rule mining is a well-known technique in data mining. It is able to reveal all interesting relationships, called associations, in a potentially large database. However, how interesting a rule is depends on the problem a user wants to solve.This paper describes new technique cluster based Master-Slave structural. It uses Hybrid technique, the grouping of bottom up and top down technique for search repetitive item sets. It reduces the time in use to get out the support count of the item sets. The Prime number show present offers the give for verify rules and provides decrease in the data complexity.
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Papers by Niket Bhargava