Refinement of Web Usage Data Clustering From K-Means with Genetic Algorithm
Web usage mining involves application of data mining techniques to discover usage patterns from the web data. Clustering is one of the important functions in web usage mining. Recent attempts have adapted the K-means clustering algorithm as well as genetic algorithms based on rough sets to find interval sets of clusters. And an important point is, so far, the researchers haven't contributed to improve the cluster quality once it is clustered. In this paper, the authors have proposed a new framework to improve the web sessions' cluster quality from k-means clustering using Genetic Algorithm (GA). Initially a modified k-means algorithm is used to cluster the user sessions. The refined initial starting condition allows the iterative algorithm to converge to a "Better" local minimum.