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

190 9 Conclusionsries commonly used in matrix-based analysis are presented accordingly, such as matrixeigenvalue, eigenvector; norm, singular value decomposition (SVD) of matrix aswell as three-way tensor expression and decomposition. In addition, a number of wellknown performance evaluation metrics in the context of information retrieval andrecommendation are reviewed, and the basic concepts in social networks are summarizedas well. The chapter of algorithms and techniques covers three main aspectsof contents - fundamental data mining algorithms, such as association rules, sequentialpatterns, Markov models and Bayesian networks, clustering and classification;Web recommendation algorithms, e.g. content-based, user-based, model-based andkNN; and the detection and evolution analysis algorithms of social networks. Then,the book presents comprehensive materials on two-level focuses of how to utilizeWeb data mining to capture the inherent cohesion between various Web pages andbetween pages and users, and how to exploit or extend Web data mining in socialand collaborative applications.The former is covered in Chapter.4, Chapter.5 and Chapter.6, in which we systematicallydiscuss the research and application issues of Web mining from the differentperspectives of Web content, linkage and usage mining. Chapter 4 presentsmaterials about Web content mining. Following the vector space model, Web searchis first addressed to cover the methodologies of crawling, archiving and indexingcontent, and searching strategies. To overcome the challenges of sparse and lowoverlapping of textual features embedded in pages, feature enrichment and latentsemantic analysis methods are given sequentially. Moreover, two extended applicationsof content analysis in automatic topic extraction and opinion mining from Webdocuments are demonstrated the application potentials in this domain. Chapter 5 ismainly talking about another important issue in Web mining, i.e. Web linkage mining.Starting with the principles of co-citation and bibliographic coupling, which isfrom information science, this chapter presents highly summarized materials on twowell-known algorithms in Web search, namely PageRank and HITS, which are thoroughlyand substantially investigated and cited in a large amount of literatures afterthe great success of Google search engine. In addition to these two algorithms, thischapter also present the topic of Web community discovery as well. The conceptsand algorithms like bipartite cores, network flow, cut-based notations of communitiesand Web community chart are substantially discussed along with the theoriesof Web graph measurement and modeling. An extended application of Web linkageanalysis for Web page classification is proposed to show how the linkage analysis facilitatesWeb page organization and presentation. Different from Chap.4 and Chap.5,in which the Web mining is mainly performed on Web pages standalone, Chapter 6reports the research and application progresses from the point of view of interactionbetween users and machines, i.e. Web usage mining. By introducing the usage datamodel of user-pageview matrix, this chapter first gives the idea of modeling usernavigation interest in terms of page-weight pair vector, and propose clustering-basedalgorithms to measure the similarity of user interest, and in turn, to find user navigationalpatterns. In additional to clustering, latent semantic analysis and its variantsare explored to be employed in Web usage mining. Two recently well studied latentsemantic analysis algorithms, namely PLSA and LDA, are presented and elaborated