CLUSTERING ALGORITHMS AND KOHONEN MAPS FOR SYMBOLIC DATA(Symbolic Data Analysis)

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概要

• 論文の詳細を見る

• This paper considers 'symbolic' data tables where variables take, as 'values', intervals, sets of categories, histograms etc. instead of single numbers or categories. After presenting some cases where this situation may occur, we concentrate on interval-type data and present methods for partitioning the underlying set of objects (rows of the data matrix) into a given number of homogeneous clusters. Our clustering strategies are typically based on a clustering criterion and generalize similar approaches in classical cluster analysis. Such methods are part of a general Symbolic Data Analysis described, e.g., in Bock and Diday (2000). Finally, we present a sequential clustering and updating strategy for constructing a Self-Organizing Map (SOM, Kohonen map) for visualizing symbolic interval-type data.

• 日本計算機統計学会の論文

著者

• Bock Hans-hermann

  Technical University Of Aachen

• Bock Hans-hermann

  Technical University Of Aachen Institute Of Statistics

関連論文

• CLUSTERING ALGORITHMS AND KOHONEN MAPS FOR SYMBOLIC DATA(Symbolic Data Analysis)
• シンボリック・データに対するクラスタリング・アルゴリズムとKohonen写像 (シンボリック・データの解析)

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