Web Data Mining

In this paper, we present an overview of research issues in web mining. We discuss mining with respect to web data referred here as web data mining. In particular, our focus is on web data mining research in context of our web warehousing project called WHOWEDA (Warehouse of Web Data). We have categorized web data mining into threes areas; web content mining, web structure mining and web usage mining. We have highlighted and discussed various research issues involved in each of these web data mining category. We believe that web data mining will be the topic of exploratory research in near future.

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

Increasing progress in numerous research fields and information technologies, led to an increase in the publication of research papers. Therefore, researchers take a lot of time to find interesting research papers that are close to their field of specialization. Consequently, in this paper we have proposed documents classification approach that can cluster the text documents of research papers into the meaningful categories in which contain a similar scientific field. Our presented approach based on essential focus and scopes of the target categories, where each of these categories includes many topics. Accordingly, we extract word tokens from these topics that relate to a specific category, separately. The frequency of word tokens in documents impacts on weight of document that calculated by using a numerical statistic of term frequency-inverse document frequency (TF-IDF). The proposed approach uses title, abstract, and keywords of the paper, in addition to the categories topics to perform the classification process. Subsequently, documents are classified and clustered into the primary categories based on the highest measure of cosine similarity between category weight and documents weights.

The number of user-contributed comments is increasing exponentially. Such comments are found widely in social media sites including internet discussion forums and news agency websites. In this paper, we summarize the current approaches to text analysis and the visualization tools which deal with opinion and topics mining of those comments. We then describe experiments for topic modeling on users' comments and examine the possible extensions of methods on visualization, sentiment analysis and opinion summarization systems.

The number of user-contributed comments is increasing exponentially. Such comments are found widely in social media sites including internet discussion forums and news agency websites. In this paper, we summarize the current approaches to text analysis and the visualization tools which deal with opinion and topics mining of those comments. We then describe experiments for topic modeling on users' comments and examine the possible extensions of methods on visualization, sentiment analysis and opinion summarization systems.

Web mining refers to the whole of data mining and related techniques that are used to automatically discover and extract information from web documents and services. When used in a business context and applied to some type of personal data, it helps companies to build detailed customer profiles, and gain marketing intelligence. Web mining does, however, pose a threat to some important ethical values like privacy and individuality. Web mining makes it difficult for an individual to autonomously control the unveiling and dissemination of data about his/her private life. To study these threats, we distinguish between 'content and structure mining' and 'usage mining.' Web content and structure mining is a cause for concern when data published on the web in a certain context is mined and combined with other data for use in a totally different context. Web usage mining raises privacy concerns when web users are traced, and their actions are analysed without their knowledge. Furthermore, both types of web mining are often used to create customer files with a strong tendency of judging and treating people on the basis of group characteristics instead of on their own individual characteristics and merits (referred to as de-individualisation). Although there are a variety of solutions to privacy-problems, none of these solutions offers sufficient protection. Only a combined solution package consisting of solutions at an individual as well as a collective level can contribute to release some of the tension between the advantages and the disadvantages of web mining. The values of privacy and individuality should be respected and protected to make sure that people are judged and treated fairly. People should be aware of the seriousness of the dangers and continuously discuss these ethical issues. This should be a joint responsibility shared by web miners (both adopters and developers), web users, and governments.

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

In recent years, the emergence of WWW (World Wide Web) led to the accumulation of huge amount of information and data. Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society. Because of such availability of huge information, utilization of the required information becomes more challenging. This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM. The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction. The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail. WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users. Web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine. It guides the search engine to provide better productive results for every search based on their importance. The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page.

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

Modern e-commerce applications require data
schemas that are constantly evolving and sparsely populated.
The Internet, e-commerce and e-business definitely hold an
important key to every organization’s future. Resting on the
innovation side of analytics, it’d be easy to lose yourself and
your business audience. Organizations can benefit more by
understanding customer’s interests and recommending the
right products to the right customer for their subsequent
purchases by identifying patterns in their earlier purchases.
This paper introduces a web data mining solution to ebusinesses
to discover hidden patterns and business strategies
from their customer demographic and web data.

This paper introduces the concept of product identity-clustering based on new similarity metrics and new performance metrics for web-crawled products. Product identity-clustering is defined here as the clustering of identical products, e.g., for price comparison purposes. Products blindly crawled over web sources, e.g., online marketplaces, have different description formats, where the features describing the same products differ in both number and representation formats. This problem causes imperfect feature vectors, where the vectors are considered to be not uniform in length and structure, with the features of various data types (numeric, categorical), and unknown vector structures. Furthermore, the product information usually contains redundant, missing, or faulty data, which are regarded as noise here. Product identity-clustering becomes a challenge when the vectors’ metadata are previously unknown and the imperfect nature of the feature vectors is considered with the occurrence of noise. In this paper, the product identity-clustering concept is introduced as a new mining metric in e-commerce. Then novel similarity metrics are introduced to improve the product identity-clustering performance of legacy metrics. Finally, novel performance metrics are proposed to measure the performance of the identity-clustering algorithms. Using these metrics, a comparison of the legacy-based similarity metrics (Euclidian, cosine, etc.) and the proposed similarity metrics is given. The results show that legacy metrics are not successful in discriminating identical web-crawled products and the proposed metrics enable better achievement in the product identity-clustering problem.

Key words: Product clustering, similarity metrics, identity clustering, performance metrics, web mining

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

Design and implementation of a research support system for web data mining has become a challenge for researchers wishing to utilize useful information on the web. This paper proposes a framework for web data mining support systems. These systems are designed for identifying, extracting, filtering and analyzing data from web resources. They combines web retrieval and data mining techniques together to provide an efficient infrastructure to support web data mining for research.

Aggression in online social networks has been studied up to now, mostly with several machine learning methods which detect such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another, is an important research topic since it can enable effective aggression monitoring, especially in media platforms which up to now apply simplistic user blocking techniques. In this paper, we focus on how to model aggression propagation on Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade (IC) and Linear Threshold (LT), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such...

The explosive growth of the Web has drastically increased information circulation and dissemination rates. As the numbers of both Web users and Web sources grow significantly every day, crucial data management issues, such as clustering on the Web, should be addressed and analyzed. Clustering has been proposed toward improving both information availability and the Web users' personalization. Clusters on the Web are either users' sessions or Web information sources, which are managed in a variation of applications and implementation test beds. This chapter focuses on the topic of clustering information over the Web in an effort to provide an overview and survey on the theoretical background and the adopted practices of the most popular emerging and challenging clustering research efforts. An up-to-date survey of the existing clustering schemes is given to be of use for both researchers and practitioners interested in the area of Web data mining.

We propose two new XML applications, XGMML and LOGML. XGMML is a graph description language and LOGML is a web-log report description language. We generate a web graph in XGMML format for a web site using the web robot of the WWWPal system (developed for web visualization and organization). We generate web-log reports in LOGML format for a web site from web log files and the web graph. In this paper, we further illustrate the usefulness of these two XML applications with a web data mining example. Moreover, we show the simplicity with which this mining algorithm can be specified and implemented efficiently using our two XML applications. We provide sample results, namely frequent patterns of users in a web site, with our web data mining algorithm.

In recent years, the emergence of WWW (World Wide Web) led to the accumulation of huge amount of information and data. Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society. Because of such availability of huge information, utilization of the required information becomes more challenging. This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM. The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction. The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail. WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users. Web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine. It guides the search engine to provide better productive results for every search based on their importance. The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page.

In recent years, the emergence of WWW (World Wide Web) led to the accumulation of huge amount of information and data. Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society. Because of such availability of huge information, utilization of the required information becomes more challenging. This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM. The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction. The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail. WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users. Web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine. It guides the search engine to provide better productive results for every search based on their importance. The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page.

Information retrieval is the process of searching within a document collection for information most relevant to a user's query. However, the type of document collection significantly affects the methods and algorithms used to process queries. In this chapter, we distinguish between two types of document collections: traditional and Web collections. Traditional information retrieval is search within small, controlled, nonlinked collections (e.g., a collection of medical or legal documents), whereas Web information retrieval is search within the world's largest and linked document collection. In spite of the proliferation of the Web, more traditional nonlinked collections still exist, and there is still a place for the older methods of information retrieval.

International Journal of Web & Semantic Technology (IJWesT) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of web & semantic technology. The growth of the World-Wide Web today is simply phenomenal. It continues to grow rapidly and new technologies, applications are being developed to support end users modern life. Semantic Technologies are designed to extend the capabilities of information on the Web and enterprise databases to be networked in meaningful ways. Semantic web is emerging as a core discipline in the field of Computer Science & Engineering from distributed computing, web engineering, databases, social networks, Multimedia, information systems, artificial intelligence, natural language processing, soft computing, and human-computer interaction. The adoption of standards like XML, Resource Description Framework and Web Ontology Language serve as foundation technologies to advancing the adoption of semantic technologies.

Web is a wide, various and dynamic environment in which different users publish their documents. Web-mining is one of data mining applications in which web patterns are explored. Studies on web mining can be categorized into three classes: application mining, content mining and structure mining. Today, internet has found an increasing significance. Search engines are considered as an important tool to respond users' interactions. Among algorithms which is used to find pages desired by users is page rank algorithm which ranks pages based on users' interests. However, as being the most widely used algorithm by search engines including Google, this algorithm has proved its eligibility compared to similar algorithm, but considering growth speed of Internet and increase in using this technology, improving performance of this algorithm is considered as one of the web mining necessities. Current study emphasizes on Ant Colony algorithm and marks most visited links based on higher amount of pheromone. Results of the proposed algorithm indicate high accuracy of this method compared to previous methods. Ant Colony Algorithm as one of the swarm intelligence algorithms inspired by social behavior of ants can be effective in modeling social behavior of web users. In addition, application mining and structure mining techniques can be used simultaneously to improve page ranking performance.

Internet has become one of the most used tools for job search. Many websites identify millions of job vacancies; then these large datasets require practical technologies and analytical methods for extraction and data cleansing process. The aim of the paper is to build up a professional profile (Business analyst) obtained by extracting data from job vacancies published into the main vertical search engine (Indeed.com), via software (Python and R), using data mining techniques, for reducing mismatch in the market labour. The professional profile obtained has been compared with the one in the Information System on Occupations Istat-Inapp.

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 0 ( 2 0 1 0 ) 16-23 a b s t r a c t This paper explores Technosocial Predictive Analytics (TPA) and related methods for Web "data mining" where users' posts and queries are garnered from Social Web ("Web 2.0") tools such as blogs, micro-blogging and social networking sites to form coherent representations of real-time health events. The paper includes a brief introduction to commonly used Social Web tools such as mashups and aggregators, and maps their exponential growth as an open architecture of participation for the masses and an emerging way to gain insight about people's collective health status of whole populations. Several health related tool examples are described and demonstrated as practical means through which health professionals might create clear location specific pictures of epidemiological data such as flu outbreaks.

Objectives: The primary objective of this research paper is to design a new and efficient clustering technique to group user navigation patterns which are useful for classification system to classify a new user with the previous users group. Methodology: Three real time web log data sets are collected from e-commerce web server, academic institution web server and a research journal web server. All three sets were collected from IIS web servers. After navigation patterns are derived from preprocessing step it is clustered into groups by using traditional Fuzzy C-Means technique. The clusters are validated and re-clustered using Bolzano_Weierstrass Theorem. Findings: Web log data is preprocessed and ICA is applied in the user session matrix to select relevant and important features. To measure the clustering accuracy of proposed and the existing methods, the parameters such as Rand Index, F measure are calculated and compared. It shows proposed BWFCM have higher rand index rate than FCM and lesser error rate. To understand the impact of the feature selection method, the data sets were implemented with the existing and proposed methods of feature selection. The parameters taken for comparison were Rand Index, Sum of Squared Errors, F-measure. The method was implemented in all the three data sets after data cleaning, session construction step. Clustering was carried out twice with the proposed clustering algorithm in all the three data sets, without selecting features and after selecting features. It was observed that the clustering results are poor when applied in full data set with irrelevant features, and the performance was increased after relevant features were selected. Conclusion: The result of the optimized clustering proves its significance and there is an increase in similarity of intra clustering and dissimilarity in inter clustering than the existing methods.

A distributed frameworks cluster is a gathering of machines that are for all intents and purposes or topographically isolated and that cooperate to give a similar services or application to customers in web application. It is conceivable that huge numbers of the services you keep running in your system today are a piece of a distributed system cluster. Cluster is a vital information mining procedure which expects to isolate the information objects into important gatherings called as groups. It is the way toward gathering objects into bunches to such an extent that items from a similar group are comparable and objects from various groups is unique. In information mining, information bunching has been examined for long time utilizing diverse calculations and ordinary patterns are proposed for better results around tailed data. The fuzzy semantic strategy is look at to group the overwhelming followed information by utilizing some technique for remove. An appraisal thinks about is introduced in view of time and exactness. In this method proposed here is evaluated to other relational clustering schemes using various propinquity matrices as input. Simulations demonstrate the scheme to be very effectual.

I n this paper, we discuss association rules which can be discovered from web data. The association rules are discussed within the scope of our WHOWEDA (warehouse of web data) project. WHOWEDA is supported by a web data niodel arid a set of algebraic operators. Web data niodel allows unijorni and integrated view of web data gathered using a user's query graph. A user's query graph describes the query by example (what user perceives as the query) and web coupling q u e 5 gathers instances of such a query graph froni the web arid store theni in the form of subgraphs (called web tuples) in a web table. We discuss ussociation rules within this doniain. An associatiori rule defines uti association betweeti the nodes arid links attributes of web tuples within a web table. There are two different classes of associatiori rules that can be developed front data in a web table. Node-to-node associations are those rules that relate the coritent (defined by nietadata attributes) between two or more nodes within a web tuple. Link associations are rules that show the cotinectivitj of different URLs. Distinguishirig the two @pes of associations provides a view of the structure of the web data. The goal of perforniing web association niitiirig on web data is to better organize searchirig patterns through hyperlinked documents.

These days, billions of Web pages are created with HTML or other markup languages. They only have a few uniform structures and contain various authoring styles compared to traditional text-based documents. However, users usually focus on a particular section of the page that presents the most relevant information to their interest. Therefore, Web documents classification needs to group and filter the pages based on their contents and relevant information. Many researches on Web mining report on mining Web structure and extracting information from Web contents. However, they have focused on detecting tables that convey specific data, not the tables that are used as a mechanism for structuring the layout of Web pages. Case modeling of tables can be constructed based on structure abstraction. Furthermore, Ripple Down Rules (RDR) is used to implement knowledge organization and construction, because it supports a simple rule maintenance based on case and local validation.

The present paper proposes a theoretical approach to a language use that is considered exceptional to the rule. Specifically, under the title ‘category invasion’, this paper investigates a phenomenon in which neighboring categories with mutually distinct prototype centers deeply invade each other’s referential territory including the prototypical core. It attempts a theoretical description of the phenomenon by analyzing the critical notions of cognitive domain and dimension and explores its linguistic mechanism. Then, a carefully devised test using Web data detects an occurrence of invasion in the language of the Web. The study aims to show that invasion is a permanent and legitimate feature of language, to be treated on a par with such established notions as synonymy, overlap, and intersection rather than be dismissed as a simple result of bad use.

The Semantic Web is considered a data integration system for different content and applications, in which every item has a specified meaning that machines can understand and process without the intervention of a human. Triple stores are the backbone of this "web of data", allowing storage and retrieval of semi-structured data as linked data usually formatted as RDF. Although there have been attempts at benchmarking the response times and query throughput of individual triple stores, there has been no systematic study of the impact of query implementation in terms of performance. In this work, we analyze the general querying process of popular triple stores and construct and measure some core metrics. Using LUBM, we choose three queries to perform experiments on a standard server and report on detailed experimental results. These results will be useful in designing future distributed systems and optimizing architectures for RDF data processing.

Abstract To gain the competitive advantage in today's age of technology, growing data and to bear the competitive pressure, making strong decisions according to customer's need and market trend has become very important. With huge amount of data on internet, web data ...

The one of the most time consuming steps for association rule mining is the computation of the frequency of the occurrences of itemsets in the database. The hash table index approach converts a transaction database to an hash index tree by scanning the transaction database only once. Whenever user requests for any Uniform Resource Locator (URL), the request entry is stored in the Log File of the server. This paper presents the hash index table structure, a general and dense structure which provides web page set extraction from Log File of server. This hash table provides information about the original database. Web Page set mining (WPs-Mine) provides a complete representation of the original database. This approach works well for both sparse and dense data distributions. Web page set mining supported by hash table index shows the performance always comparable with and often better than algorithms accessing data on flat files. Incremental update is feasible without reaccessing the original transactional database.

The explosive growth of the web has drastically changed the way in which information is managed and accessed. The large-scale of web data sources and the wide availability of services over the internet have increased the need for effective web data mining techniques and mechanisms . A sophisticated method to organize the layout of the information and assist user navigation

The one of the most time consuming steps for association rule mining is the computation of the frequency of the occurrences of itemsets in the database. The hash table index approach converts a transaction database to an hash index tree by scanning the transaction database only once. Whenever user requests for any Uniform Resource Locator (URL), the request entry is stored in the Log File of the server. This paper presents the hash index table structure, a general and dense structure which provides web page set extraction from Log File of server. This hash table provides information about the original database. Web Page set mining (WPs-Mine) provides a complete representation of the original database. This approach works well for both sparse and dense data distributions. Web page set mining supported by hash table index shows the performance always comparable with and often better than algorithms accessing data on flat files. Incremental update is feasible without reaccessing the original transactional database.

Web is a collection of interrelated files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, E-government, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper, we have studied the basic concepts of web mining, classification, processes and issues. In addition to this, this paper also analyzed the web mining research challenges.

The cost of acquiring training data instances for induction of data mining models is one of the main concerns in real-world problems. The web is a comprehensive source for many types of data which can be used for data mining tasks. But the distributed and dynamic nature of web dictates the use of solutions which can handle these characteristics. In this paper, we introduce an automatic method for topical data acquisition from the web. We propose a new type of topical crawlers that use a hybrid link context extraction method for topical crawling to acquire on-topic web pages with minimum bandwidth usage and with the lowest cost. The new link context extraction method which is called Block Text Window (BTW), combines a text window method with a block-based method and overcomes challenges of each of these methods using the advantages of the other one. Experimental results show the predominance of BTW in comparison with state of the art automatic topical web data acquisition methods based on standard metrics.

Academic industries used to collect feedback from the students on the main aspects of course such as preparations, contents, delivery methods, punctual, skills, appreciation, and learning experience. The feedback is collected in terms of both qualitative and quantitative scores. Recent approaches for feedback mining use manual methods and it focus mostly on the quantitative comments. So the evaluation cannot be made through deeper analysis. In this paper, we develop a student feedback mining system (SFMS) which applies text analytics and sentiment analysis approach to provide instructors a quantified and deeper analysis of the qualitative feedback from students that will improve the students learning experience. We have collected feedback from the students and then text processing is done to clean the data. Features or topics are extracted from the pre-processed document. Feedback comments about each topic are collected and made as a cluster. Classify the comments using sentiment classifier and apply the visualization techniques to represent the views of students. This proposed system is an efficient approach for providing qualitative feedback for the instructor that enriches the students learning.

Web mining is a computation intensive task, even after the mining tool itself has been developed. Most mining software are developed ad-hoc and usually are not scalable nor reused for other mining tasks. The objective of this paper is to present a model for fast Web mining prototyping, referred to as WIM -Web Information Mining. The underlying conceptual model of WIM provides its users with a level of abstraction appropriate for prototyping and experimentation throughout the Web data mining task. Abstracting from the idiosyncrasies of raw Web data representations facilitates the inherently iterative mining process. We present the WIM conceptual model, its associated algebra with a set of operators, and the WIM tool software architecture, which implements the WIM model. We also illustrate how the model can be applied to real Web data mining tasks. The experimentation of WIM in real use cases has shown to significantly facilitate Web mining prototyping.

Deep web contents are accessed by queries submitted to web databases and the returned data records are enwrapped in dynamically generated web pages (they will be called deep Web pages). Extracting structured data from deep web pages is a challenging problem due to the underlying intricate structures of such pages. As the popular two-dimensional media, the contents on web pages are always displayed regularly for users to browse. This motivates us to seek a different way for deep web data extraction to overcome the limitations of previous works by utilizing some interesting common visual features on the deep web pages. In this paper, an agent based authentication mechanism is proposed which uses an agent program runs on the server to authenticate the user. A novel vision-based approach that is web page programming language-independent is also proposed. This approach primarily utilizes the visual features on the deep web pages to implement deep web data extraction, including data record extraction and data item extraction. Our experiments on a large set of web databases show that the proposed vision-based approach is highly effective for deep web data extraction and provides security for the registered user. Keywords— Authentication, vision based approach, web data, agent program, web page.

Nowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is providing different categories videos including Science and Technology, Films and Animation, News and politics, Movies, Comedy, Sports, Music etc. Each video hosted in website such as YouTube have its own identity and features. The identity and features of each video can be described by web video metadata objects such as- URL of each video, category, length of the video, rating information, view counts, comment information, key words etc. Using extracted web video metadata objects, we present an in-depth and systematic clustering study on the metadata objects of YouTube videos using Expectation Maximization (EM) and Density Based (DB) clustering approach. Distinct web video metadata object clusters are formed based on different category of web videos. The resultant clusters are analyzed in depth as a step in the KDD process.