Improved K-MEAN Clustering Approach for Web Usage Mining

2011 International Conference on Computer and Management (CAMAN), 2011

In this paper we present clustering method is very sensitive to the initial center values ,requirements on the data set too high, and cannot handle noisy data the proposal method is using information entropy to initialize the cluster centers and introduce weighting parameters to adjust the location of cluster centers and noise problems. The navigation datasets which are sequential in nature, Clustering web data is finding the groups which share common interests and behavior by analyzing the data collected in the web servers, this improves clustering on web data efficiently using improved fuzzy c-means(FCM) clustering. Web usage mining is the application of data mining techniques to web log data repositories. It is used in finding the user access patterns from web access log. Web data Clusters are formed using on MSNBC web navigation dataset.

The internet has progressed with a wide range of information over last few eras. This also brings the issue of information flooding and may cause the interruption to the user when accessing pertinent information. So it becomes a challenging task for the webmasters to provide the search options according to the individual user. Therefore, Web usage mining is an efficient technique where the helpful information is extracted from the user's history stored in the server logs and clusters those logs into a variety of labels. Hence the proposed system used the CHAMELEON clustering algorithm which processes the search logs of the user and cluster those logs into separate clusters based on the search category. Finally the chameleon algorithm is compared with other data clustering algorithms in terms of the Sum of Score Squared Error (SSE), rand index and F-measure respectively.

2016

Data reduction is the process of minimizing the amount of data that needs to be stored in a data storage environment. Data reduction can increase storage efficiency and reduce costs. Data cleaning perform in the Data Preprocessing and Web Usage Mining. The work on data cleaning of web server logs, irrelevant items and useless data can not completely removed and Overlapped data causes difficulty during data retrieving from database. In this paper, we introduce Ant Based Pattern Clustering Algorithm to get pattern data for mining .It also presents Log Cleaner that can filter out much irrelevant, inconsistent data based on the common of their URLs. IndexTerms— Data Mining, Clustering, Data Reduction, Ant based clustering, Web usage Mining.

In process of knowledge discovery from any web-log dataset, most widely and extensively used clustering algorithm for this purpose is Fuzzy c-means (FCM) algorithm because the data of web-log is unsupervised dataset. Due to sensitivity of FCM, it can be easily trapped in a local optimum, and it is also depends on initialization. In this paper we present use of Genetic algorithm in Fuzzy c-means algorithm to select initial center point for clustering in FCM. The purpose of this paper is to provide optimum initial solution for FCM with the help of genetic algorithm to reduce the error rate in pattern creation.

World Wide Web (WWW) has increasing information, and the websites usage also increasing rapidly. The web server records the users' search activity on the web and store it in the server log. When these web logs properly mined, lot of useful information can be provided for the decision making. Thus the process of accessing relevant information becomes easier. The web log mining usually used for the purpose of generating a model for search pattern discovery with the semantic meaning. Hence the CHAMELEON clustering algorithm is used for the clustering the user search logs based on the search category. Semantic analysis is performed in addition on the clustered logs to extract the semantically similar patterns. This process of discovering the frequent search pattern based on semantic relation can be used for providing user pertinent information using web usage mining. Therefore, the proposed system brings the frequent patterns as output with semantical information that supports the user to understand the pattern semantically.

2014

There are many models in literature and practice that analyse user behaviour based on user navigation data and use clustering algorithms to characterize their access patterns. The navigation patterns identified are expected to capture the user’s interests. In this paper, we model user behaviour as a vector of the time the user spends at each URL, and further classify a given new user access pattern. The clustering and classification methods of k-means with non-Euclidean similarity measure, Bayesian classifiers and artificial neural networks, with standardised fuzzy inputs are implemented and compared. Apart from identifying user behaviour, the model can also be used as a prediction system where we can identify deviational behaviour. Keywords:

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. (Abstract)

2014

An absolute framework and findings are presented in mining web usage patterns from web log files of a real web site that has all the challenging aspects of real-life web usage mining, including evolving user profiles and external data describing ontology of the web content. The behavior of a web site's users may change so quickly that attempting to make predictions, according to the frequent patterns coming from the analysis of an access log file, becomes challenging. In order for the obsolescence of the behavioral patterns to become as possible, the ideal method would provide frequent patterns in real time, allowing the result to be available immediately. Even though the web site under study is part of a nonprofit organization that does not "sell" any products, it was crucial to understand "who" the users Ire, "what" they looked at, and "how their interests changed with time," all of which are important questions in Customer Relationship ...

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.