Web Log Data Analysis by Enhanced Fuzzy C Means Clustering
This work addresses the need to reduce browsing time for web users by enhancing web usage mining, though it appears incremental in nature.
The paper tackles the problem of analyzing web log data to understand user behavior by proposing a novel clustering method for partitioning user sessions, which shows improved accuracy and performance measures compared to existing methods.
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.