  Clustering techniques are widely used in Web Usage Mining to capture similar
interests and trends among users accessing a Web site. For this purpose, web
access logs generated at a particular web site are preprocessed to discover the
user navigational sessions. Clustering techniques are then applied to group the
user session data into user session clusters, where intercluster similarities
are minimized while the intra cluster similarities are maximized. Since the
application of different clustering algorithms generally results in different
sets of cluster formation, it is important to evaluate the performance of these
methods in terms of accuracy and validity of the clusters, and also the time
required to generate them, using appropriate performance measures. This paper
describes various validity and accuracy measures including Dunn's Index, Davies
Bouldin Index, C Index, Rand Index, Jaccard Index, Silhouette Index, Fowlkes
Mallows and Sum of the Squared Error (SSE). We conducted the performance
evaluation of the following clustering techniques: k-Means, k-Medoids, Leader,
Single Link Agglomerative Hierarchical and DBSCAN. These techniques are
implemented and tested against the Web user navigational data. Finally their
performance results are presented and compared.
