Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

130 6 Web Usage Miningap k =∑s i ∈R kθ i,k · s i|R k |(6.30)where |R k | is the number of the chosen user sessions in R k .Step 3: Output a set of task-specific user access patterns TAP corresponding to t tasks,TAP = {ap k ,k = 1,···,t}. In this expression, each user access pattern is represented by aweighted page vector, where the weights indicate the relative visit preferences of pages exhibitedby all associated user sessions for this task-specific access pattern.6.4 Co-Clustering Analysis of weblogs using Bipartite SpectralProjection ApproachIn previous sections, we broadly discussed Web clustering in Web usage mining. BasicallyWeb clustering could be performed on either Web pages or user sessions in the context of Webusage mining. Web page clustering is one of popular topics in Web clustering, which aimsto discover Web page groups sharing similar functionality or semantics. For example, [114]proposed a technique LSH (Local Sensitive Hash) for clustering the entire Web, concentratingon the scalability of clustering. Snippet-based clustering is well studied in [92]. [147] reportedusing a hierarchical monothetic document clustering for summarizing the search results. [121]proposed a Web page clustering algorithm based on measuring page similarity in terms ofcorrelation. In contrast to Web page clustering, Web usage clustering is proposed to discoverWeb user behavior patterns and associations between Web pages and users from the perspectiveof Web user. In practice, Mobasher et al. [184] combined user transaction and pageviewclustering techniques, which was to employ the traditional k-means clustering algorithm tocharacterize user access patterns for Web personalization based on mining Web usage data. In[258] Xu et al. attempted to discover user access patterns and Web page segments from Weblog files by utilizing a so-called Probabilistic Semantic Latent Analysis (PLSA) model.The clustering algorithms described above are mainly manipulated on one dimension (orattribute) of the Web usage data only, i.e. user or page solely, rather than taking into accountthe correlation between Web users and pages. However, in most cases, the Web object clustersdo often exist in the forms of co-occurrence of pages and users - the users from the samegroup are particularly interested in one subset of Web pages. For example, in the contextof customer behavior analysis in e-commerce, this observation could correspond to the phenomenonthat one specific group of customers show strong interest to one particular categoryof goods. In this scenario, Web co-clustering is probably an effective means to address thementioned challenge. The study of co-clustering is firstly proposed to deal with co-clusteringof documents and words in digital library [73]. And it has been widely utilized in many studieswhich involved in multiple attribute analysis, such as social tagging system [98] and geneticrepresentation [108] etc. In this section, we will propose a co-clustering algorithm for Webusage mining based on bipartite spectral clustering.