Feature selection is essential topic in data mining. Although its importance, most studies of fea... more Feature selection is essential topic in data mining. Although its importance, most studies of feature selection are limited to batch learning. The online feature selection is used to make accurate prediction for using small number and fixed number of active features. We deal with this challenge by studying scarcity regularization and truncation techniques. we estimate the performance of proposed algorithms for online feature selection and its applications.we propose Two-Gaussian algorithm for clustering a search result. Our predicted informations are separately grouped in the basis of classification. This clustering technique done by using Two Gaussian mixtures algorithm. And we implement Blowfish algorithm for reduce privacy issues. All informations are stored in our system as an encrypted format. And also we implement public auditing for audit a user contents. Because it is used to avoid fake informations.
Data mining refers to extracting knowledge from large amount of data. Market basket analysis is a... more Data mining refers to extracting knowledge from large amount of data. Market basket analysis is a data mining technique to discover associations between datasets. Association rule mining identifies relationship between a large set of data items. When large quantity of data is constantly obtained and stored in databases, several industries is becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can help in catalog design, cross-marketing and various businesses decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased by c...
This paper is about the literature survey done on the various significant Applications of Data Mi... more This paper is about the literature survey done on the various significant Applications of Data Mining. Many fields have recognized that application of Data Mining Tools and Techniques have yielded a better result for their useful decision making. Now, since there is a large explosion of data and the data & information are becoming too complex, a better and efficient Data Mining Techniques have to be thought of. Hence, through this survey, one could understand how Data Mining has helped various applications in finding useful knowledges. 1. APPLICATION OF DATA MINING IN BUSINESS INTELLIGENCE Typical business intelligence applications of data mining include Risk Analysis (given a set of current customers and their finance/insurance history data, build a predictive model that can be used to classify a new customer into a risk category), Targeted Marketing (given a set of current customers and history on their purchases and their responses to promotions, target new promotions to those mo...
As an expanding amount of social networking data is published and shared for marketable and resea... more As an expanding amount of social networking data is published and shared for marketable and research purposes, privacy issues regarding the individuals in social networks have become serious concerns. A social network is a social arrangement made up of a set of social actors (such as individuals or organizations) and a set of the dyadic ties between these actors. The social network point of view provides a set of methods for analyzing the structure of whole social entities as well as a range of theories explaining the patterns observed in these structures. The main issues in social networks are privacy. Our existing system provides privacy for social networks, but it more complex, because it uses multiple algorithms. So propose a new enhancement, which is combine two algorithms, and create a new algorithm.
Feature selection is essential topic in data mining. Although its importance, most studies of fea... more Feature selection is essential topic in data mining. Although its importance, most studies of feature selection are limited to batch learning. The online feature selection is used to make accurate prediction for using small number and fixed number of active features. We deal with this challenge by studying scarcity regularization and truncation techniques. we estimate the performance of proposed algorithms for online feature selection and its applications.we propose Two-Gaussian algorithm for clustering a search result. Our predicted informations are separately grouped in the basis of classification. This clustering technique done by using Two Gaussian mixtures algorithm. And we implement Blowfish algorithm for reduce privacy issues. All informations are stored in our system as an encrypted format. And also we implement public auditing for audit a user contents. Because it is used to avoid fake informations.
Data mining refers to extracting knowledge from large amount of data. Market basket analysis is a... more Data mining refers to extracting knowledge from large amount of data. Market basket analysis is a data mining technique to discover associations between datasets. Association rule mining identifies relationship between a large set of data items. When large quantity of data is constantly obtained and stored in databases, several industries is becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can help in catalog design, cross-marketing and various businesses decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased by c...
This paper is about the literature survey done on the various significant Applications of Data Mi... more This paper is about the literature survey done on the various significant Applications of Data Mining. Many fields have recognized that application of Data Mining Tools and Techniques have yielded a better result for their useful decision making. Now, since there is a large explosion of data and the data & information are becoming too complex, a better and efficient Data Mining Techniques have to be thought of. Hence, through this survey, one could understand how Data Mining has helped various applications in finding useful knowledges. 1. APPLICATION OF DATA MINING IN BUSINESS INTELLIGENCE Typical business intelligence applications of data mining include Risk Analysis (given a set of current customers and their finance/insurance history data, build a predictive model that can be used to classify a new customer into a risk category), Targeted Marketing (given a set of current customers and history on their purchases and their responses to promotions, target new promotions to those mo...
As an expanding amount of social networking data is published and shared for marketable and resea... more As an expanding amount of social networking data is published and shared for marketable and research purposes, privacy issues regarding the individuals in social networks have become serious concerns. A social network is a social arrangement made up of a set of social actors (such as individuals or organizations) and a set of the dyadic ties between these actors. The social network point of view provides a set of methods for analyzing the structure of whole social entities as well as a range of theories explaining the patterns observed in these structures. The main issues in social networks are privacy. Our existing system provides privacy for social networks, but it more complex, because it uses multiple algorithms. So propose a new enhancement, which is combine two algorithms, and create a new algorithm.
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Papers by Grace Padma