Search

Web Usage Mining is the application of data mining techniques to web usage log repositories in order to discover the usage patterns that can be used to analyze the users navigational behavior. During the preprocessing stage, raw web log data is transformed into a set of user profiles. Each user profile captures a set of URLs representing a user session. Clustering can be applied to this sessionized data in order to capture similar interests and trends among users navigational patterns. Since the sessionized data may contain thousands of user sessions and each user session may consist of hundreds of URL accesses, dimensionality reduction is achieved by eliminating the low support URLs. Very small sessions are also removed in order to filter out the noise from the data. But direct elimination of low support URLs and small sized sessions may results in loss of a significant amount of information especially when the count of low support URLs and small sessions is large. We propose a fuzzy solution to deal with this problem by assigning weights to URLs and user sessions based on a fuzzy membership function. After assigning the weights we apply a Fuzzy c-Mean Clustering algorithm to discover the clusters of user profiles. In this paper, we describe our fuzzy set theoretic approach to perform feature selection (or dimensionality reduction) and session weight assignment. Finally we compare our soft computing based approach of dimensionality reduction with the traditional approach of direct elimination of small sessions and low support count URLs. Our results show that fuzzy feature evaluation and dimensionality reduction results in better performance and validity indices for the discovered clusters.

The explosive growth of World Wide Web (WWW) has necessitated the development of Web personalization systems in order to understand the user preferences to dynamically serve customized content to individual users. To reveal information about user preferences from Web usage data, Web Usage Mining (WUM) techniques are extensively being applied to the Web log data. Clustering techniques are widely used in WUM to capture similar interests and trends among users accessing a Web site. Clustering aims to divide a data set into groups or clusters where inter-cluster similarities are minimized while the intra cluster similarities are maximized. This paper reviews four of the popularly used clustering techniques: k-Means, k-Medoids, Leader and DBSCAN. These techniques are implemented and tested against the Web user navigational data. Performance and validity results of each technique are presented and compared., Comment: arXiv admin note: substantial text overlap with arXiv:1507.03340

The World Wide Web continues to grow at an amazing rate in both the size and complexity of Web sites and is well on its way to being the main reservoir of information and data. Due to this increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users. To design popular and attractive websites publishers must understand their users needs. Therefore analyzing users behaviour is an important part of web page design. Web Usage Mining (WUM) is the application of data mining techniques to web usage log repositories in order to discover the usage patterns that can be used to analyze the users navigational behavior. WUM contains three main steps: preprocessing, knowledge extraction and results analysis. The goal of the preprocessing stage in Web usage mining is to transform the raw web log data into a set of user profiles. Each such profile captures a sequence or a set of URLs representing a user session.

Clustering techniques are widely used in Web Usage Mining to capture similar interests and trends among users accessing a Web site. For this purpose, web access logs generated at a particular web site are preprocessed to discover the user navigational sessions. Clustering techniques are then applied to group the user session data into user session clusters, where intercluster similarities are minimized while the intra cluster similarities are maximized. Since the application of different clustering algorithms generally results in different sets of cluster formation, it is important to evaluate the performance of these methods in terms of accuracy and validity of the clusters, and also the time required to generate them, using appropriate performance measures. This paper describes various validity and accuracy measures including Dunn's Index, Davies Bouldin Index, C Index, Rand Index, Jaccard Index, Silhouette Index, Fowlkes Mallows and Sum of the Squared Error (SSE). We conducted the performance evaluation of the following clustering techniques: k-Means, k-Medoids, Leader, Single Link Agglomerative Hierarchical and DBSCAN. These techniques are implemented and tested against the Web user navigational data. Finally their performance results are presented and compared.