様々なクラスタリング手法について

science network paper

DATA CLUSTERING: FROM DOCUMENTS TO THE WEB
Dusan Husek, Jaroslav Pokorny, Hana Rezankova, Vaclav Snasel
Accepted as a chapter of the book: Web Data Management Practices: Emerging Techniques and Technologies, (Eds. Athena Vakali, George Pallis), Idea Group Inc., to appear 2006.

Abstract:
The chapter provides a survey of some clustering methods relevant to the clustering document collections and, in consequence, Web data. We start with classical methods of cluster analysis which seem to be relevant in approaching to cluster Web data. The graph clustering is also described since its methods contribute significantly to clustering Web data. A use of artificial neural networks for clustering has the same motivation. Based on previously presented material, the core of the chapter provides an overview of approaches to clustering in the Web environment. Particularly, we focus on clustering web search results, in which clustering search engines arrange the search results into groups around a common theme. We conclude with some general considerations concerning the justification of so many clustering algorithms and their application in the Web environment.

簡単なクラスタリング手法の説明と参考文献が載っている．
使いたい手法について詳細に調べるとき便利．

  • Introduction
  • Methods of Cluster Analysis
    • Dissmilarity and similarity measures
    • Partitioning algorithms
    • Hierarchical algorithms
    • Two-way joining algorithm
    • Subspace clustering
  • Graph Clustering
    • Linear algebra background
    • Eigenvector clustering of graphs
    • Connectivity clustering of graphs
    • Combined methods
  • Artifical Neural Networks
    • Layered, feed-forward, backpropagation neural networks
    • Self-organizing neural networks
    • Recurrent ANNs
  • Web Clustering
    • Application of Web clustering
    • Principles of Web clustering methods
    • Classification of Web clustering methods
    • Clustering with snippets
  • Conclustion