Abstract—Owing to the popularity of World Wide Web, many enterprises have changed the ways of doing business, which enhance the rapid development of E-commerce directly and makes the development of web usage mining skills important. It becomes a crucial issue to predict exactly the ways how users and customers browse websites. The prediction result can be used for personalization, building proper websites, promotion, getting marketing information, and forecasting market trends etc. Markov model is assumed to be a probability model by which users ’ browsing behaviors can be predicted at category level. Bayesian theorem can also be applied to present and infer users ’ browsing behaviors at webpage level. In this research, Markov models and Bayesian theorem are combined and a two-level prediction model is designed. By the Markov Model, the system can effectively filter the possible category of the websites and Bayesian theorem will help to predict websites accuracy. The experiments will show that our provided model has noble hit ratio for prediction.

Abstract. 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 is therefore particularly important. In this work, we focus on web usage mining, applying data mining techniques to web server logs. Web usage mining is the non-trivial process of distinguishing implicit, previously unknown but potentially useful clickstream patterns that may exist in any collection of web access logs. The required abstraction can be generated by clustering the web access logs based on some sort of similarity measure. Clustering is done such that the web access logs within the same group or cluster are more similar than data points from different clusters. In this chapter, we propose a partitional algorithm namely Multi Pass Combined Standard Deviation(CSD) Means algorithm which automatically generates the optimum number of clusters from the web clickstream patterns. The quality of clusters obtained using these algorithms are compared using K-Means algorithm, Rough K-Means algorithm and model based algorithms ANTCLUST and ACCANTCLUST. The experimental analysis of mined clickstream patterns shows the effectiveness of the proposed algorithm.

Abstract. The proliferation of internet along with the attractiveness of the web in recent years has made web mining as the research area of great magnitude. Web mining essentially has many advantages which makes this technology attractive to researchers. The analysis of web user’s navigational pattern within a web site can provide useful information for applications like, server performance enhancements, restructuring a web site, direct marketing in e-commerce etc. The navigation paths may be explored based on some similarity criteria, in order to get the useful inference about the usage of web. The objective of this paper is to propose an effective clustering technique to group users ’ sessions by modifying K-means algorithm and suggest a method to compute the distance between sessions based on similarity of their web access path, which takes care of the issue of the user sessions that are of variable length. Keywords: web mining, clustering; K-means, Jaccard Index. 1