WEB PAGE ACCESS PREDICTION USING FUZZY CLUSTERING BY LOCAL APPROXIMATION MEMBERSHIPS (FLAME) ALGORITHM

ABSTRACT Web page prediction is a technique of web usage mining used to predict the next set of web pages that a user may visit based on the knowledge of previously visited web pages. The World Wide Web (WWW) is a popular and interactive medium for publishing the information. While browsing the web, users are visiting many unwanted pages instead of targeted page. The web usage mining techniques are used to solve that problem by analyzing the web usage patterns for a web site. Clustering is a data mining technique used to identify similar access patterns. If mining is done on those patterns, recommendation accuracy will be improved rather than mining dissimilar access patterns. The discovered patterns can be used for better web page access prediction. Here, two different clustering techniques, namely Fuzzy C-Means (FCM) clustering and FLAME clustering algorithms has been investigated to predict the webpage that will be accessed in the future based on the previous action of browsers behavior. The Performance of FLAME clustering algorithm was found to be better than that of fuzzy C-means, fuzzy K-means algorithms and fuzzy self-organizing maps (SOM). It also improves the user browsing time without compromising prediction accuracy

A Clustering Based User-Centered (CBUC) Approach for Integrating Social Data into Groups of Interest

Social web sites by means of huge database websites like Facebook, Twitter and, Linked have been becomes a very important task for day to day life. Thousands and millions of social users are extremely linked from each other to these social websites in favor of networking, conversing, distributing, and sharing by means of each other. Social network sites contain consequently develop into a great source of contents of interest, part of which might reduce into the scope of interests of a known group. Therefore no well-organized solution has been proposed in recent works for a grouping of social users depending on their interest’s information, particularly when they are confined by and speckled across diverse social network sites. Clustering Based User-Centered (CBUC) approach is proposed for integrating social data into groups of interests. Proposed CBUC approach follows the procedure of Modified Fuzzy C Means (MFCM) clustering for social grouping of social data user into different group based on their searching interest. This CBUC approach allows users grouping of user social data from various social network sites such as Facebook, Twitter, and LinkedIn by means of their respective groups of interest. CBUC approach the users are clustered by converting of individual social data interest into fuzzification value and verified using the fuzzy objective function. Additional, to reduce the number of iterations, the proposed CBUC approach, MFCM initializes the centroid by means of dist-max initialization algorithm earlier than the execution of MFCM algorithm iteratively. In this approach the users are also capable to personalize their sharing settings and interests contained by their individual groups related to their own preferences. CBUC approach makes it potential in the direction of aggregate social information of the group’s members and extracts from these data the information appropriate to the group's subject of interests. Furthermore, it follows a CBUC design permitting each member in the direction of personalize his/her sharing situation and interests surrounded by their individual groups

The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach

To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception

FARS: Fuzzy Ant based Recommender System for Web Users

Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on collaborative behavior of ants (FARS). FARS works in two phases: modeling and recommendation. First, user’s behaviors are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations

Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio