Improved FCM Algorithm for Clustering on Web Usage Mining

World Wide Web is a huge repository of information and there is a tremendous increase in the volume of information daily. The number of users are also increasing day by day. To reduce users browsing time lot of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range of applications like Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Clustering is the grouping of similar instances or objects. The key factor for clustering is some sort of measure that can determine whether two objects are similar or dissimilar. In this paper a novel clustering method to partition user sessions into accurate clusters is discussed. The accuracy and various performance measures of the proposed algorithm shows that the proposed method is a better method for web log mining.

2009

In the k means clustering algorithm right value of clusters (k) are initially unknown and effective selections of initial seed are also difficult. In this paper efficient k-means algorithm is proposed and implemented which overcome initial seed problem and unknown number of cluster problem. The algorithm is applied on real BIST server log data and Gaussian dataset to test its accuracy and efficiency. At application level this algorithm may used for efficient knowledge discovery from web repositories.

The explosive growth in the information available on the Web has prompted the need for developing Web personalization systems that understand and exploit user preferences to dynamically serve customized content to individual users [1]. To reveal information about user preferences from Web usage data, Data Mining techniques can be naturally applied, leading to the so-called Web Usage Mining (WUM) [2]. Clustering is widely used in WUM to capture similar interests and trends among users accessing a Web site [3]. k-Means clustering is a popular clustering algorithm based on the partitioning of data. However one of the drawbacks of it is that it requires the user to specify the number of clusters at the beginning and also it is sensitive to the initial selection of cluster centres. The global k-Means algorithm proposed by Likas [4] provides an incremental approach to clustering by dynamically adding one cluster centre at a time through a deterministic global search procedure. It does not depend on any initial conditions and considerably outperforms the k-Means algorithms, but the problem associated with this algorithm is its heavy computational effort. A faster version of global k-Means algorithm substantially reduces the execution time by improving the way of creating the next cluster centre in the global k-Means algorithm. We implemented and tested these algorithms against the web usage data in order to discover the user navigational session clusters. In this paper we present the implementation details of each of the above mentioned k-Means clustering techniques along with the underlying mathematical foundations. The results are presented with a comparison of different techniques. Our results show that the fast global k-Means clustering algorithm significantly reduces the computational time without affecting the performance of the global k-Means algorithm. It also outperforms the global K-means algorithm in terms of validity measure.

VAWKUM Transactions on Computer Sciences

The Web contributes greatly to our life in many fields such as education, entertainment, Internet banking, online shopping and software downloading. This has led to rapid growth in the number of Internet users, which resulting in an explosive increase in traffic or bottleneck over the Internet performance. This paper proposes a new approach to group users according to their Web access patterns. The proposed approach for grouping users is based on Fuzzy c-means technique, which allows web users to be assigned into more than one cluster or interest. Each web user has a degree of membership of belonging to each cluster. The experimental results showed that the web users were successfully clustered to similar groups very fast using Fuzzy-c-means. In addition, the Fuzzy-c-means performed well and became much better when the clusters number increased on two real Bo2 and NY datasets. The proposed intelligent web users clustering based on Fuzzy-c-means can be used for discovering users' interests in Web pages that can contribute in enhancing several approaches such as Web caching, Web pre-fetching and Web recommender systems that are recently used to improve the Web performance.

2014

Web log mining is an emerging part of data mining. It provides invaluable information by discovering trends and regularities in web user's access patterns. Clustering based on access pattern is an important research topic of web usage mining. Knowledge obtained from web user clusters has been used in different fields of web mining technologies. This paper presents an algorithm for measuring similarities and automated segmentation of web users based on their past access patterns. The compatibility measures are based on content extracted from user's browser data. Furthermore it also provides a locality based clustering method for the people who are unknown to their most compatible friends.

Due to the continuous proliferation of e-businesses, there is intense competition among organizations to attract and retain customers. Analyses of the web server logs of these organizations are critical for obtaining insights into web usage behavior, which can support the design of more attractive web structures. In this study, we propose a mountain density function (MDF)-based fuzzy clustering framework for discovering user session clusters in web log data. The major steps in this framework include web log preprocessing, MDF-based discovery of fuzzy user session clusters, and validation of these clusters. To consider the high dimensionality of user session data, we propose a fuzzy approach for assigning weights to user sessions. Fuzzy c-means (FCM) and fuzzy c-medoids (FCMed) algorithms are used to cluster the user sessions. The selection of suitable initial cluster centers is a major challenge for these methods, so we propose MDF-based FCM (MDFCM) and FCMed (MDFCMed) algorithms to overcome this problem. MDF-based clustering is also used to estimate the number of clusters. Our results clearly indicate that the quality of the clusters formed using the proposed algorithms is much better in terms of various validity measures compared with the FCM and FCMed algorithms.

Integrated Intelligent Research, 2012

Recommendation system plays a major role in web mining and it is applied to many applications such as e-commerce, e-government and e-library. The key challenges of recommendation system is to recommend the users based on their interest among more visitors and huge information. To make this challenge effective, there is a need for clustering algorithm to handle the data. Hence, this research focused on designing effective clustering algorithm to apply it in e-commerce applications. The grey wolf optimization based clustering is proposed to make an efficient clustering method for grouping the users based on their interest. To find the effective clustering, proposed a grey wolf optimization based fuzzy clustering algorithm, and made a comparison on Fuzzy C Means (FCM) based Genetic Algorithm (GA), Entropy based FCM and Improved Genetic FCM (FCM-GA). The experimental results proves that it performs better than traditional algorithms, at the same time the quality is improved.

The explosive growth in the information available on the Web has necessitated the need for developing Web personalization systems that understand user preferences to dynamically serve customized content to individual users. Web server access logs contain substantial data about the accesses of users to a Web site. Hence, if properly exploited, the log data can reveal useful information about the navigational behaviour of users in a site. 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 analyse the user's navigational behaviour. Web Usage Mining consists of three main steps: preprocessing, knowledge extraction and results analysis. 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 "Modified Mountain Clustering" algorithm to discover the clusters of user profiles. Our results show that fuzzy feature evaluation results in better performance and validity indices for the discovered clusters.

Analysis of web server logs of e-business organisations is critical to provide insight into users' web usage behaviour which can assist in designing most attractive websites. In this article, a mountain density function (MDF)-based fuzzy clustering framework to discover user session clusters from web logs is proposed. Major steps in this framework include web log preprocessing, MDF-based discovery of user session clusters and their validation. To deal with high dimensionality of user sessions, a fuzzy approach for assigning weights to user sessions has been proposed. For the discovery of user session clusters, fuzzy c-means (FCM) and fuzzy c-medoids (FCMed) algorithms are explored. Since the selection of suitable initial cluster centres is a big challenge, MDF-based fuzzy c-means (MDFCM) and fuzzy c-medoids (MDFCMed) algorithms are proposed to overcome this problem. Our results show that quality of clusters formed using MDFCM/MDFCMed is much better than FCM and FCMed.

International Journal of Computer Applications, 2014

Web Usage Mining used to extract knowledge from WWW. Nowadays interaction of user towards web data is growing, web usage mining is significant in effective website management, adaptive website creation, support services, personalization, and network traffic flow analysis and user trend analysis and user's profile also helps to promote website in ranking. Agglomerative clustering is a most flexible method and it is also used for clustering the web data in web usage mining, there are do not need the number of clusters as a input. Agglomerative have many drawbacks such as initial error propagation, dimensionality, complexity and data set size issues. In this paper we have introduced solution for data set size problem that helpful for information retrieve from large web data, web log data files are as a input for agglomerative clustering algorithms and output is efficient clustering that will be used further for information extraction in web usage mining.