カテゴリ写像に基づく追加学習に対応可能な自己組織化とWebニュース群の動的クラスタリングへの応用

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2012/08/01

タイトル(英語): Self-Organization with Additional Learning Based on Category Mapping and Its Application to Dynamic News Clustering

著者名: 豊田　哲也（筑波大学 大学院システム情報工学研究科／日本学術振興会），延原　肇（筑波大学 システム情報系）

著者名(英語): Tetsuya Toyota (Graduate School of Systems and Information Engineering, University of Tsukuba/Research Fellow of Japan Society for the Promotion of Science), Hajime Nobuhara (Faculty of Engineering, Information and Systems, University of Tsukuba)

キーワード: 自己組織化マップ，テキストマイニング，テキストクラスタリング，情報可視化，Wikipedia　　Self-Organizing Map(SOM)，Text Mining，Text Clustering，Visualization，Wikipedia

要約(英語): The Internet news are texts which involve from various fields, therefore, when a text data that will show a rapid increase of the number of dimensions of feature vectors of Self-Organizing Map (SOM) is added, these results cannot be reflected to learning. Furthermore, it is difficult for users to recognize the learning results because SOM can not produce any label information by each cluster. In order to solve these problems, we propose SOM with additional learning and dimensional by category mapping which is based on the category structure of Wikipedia. In this method, input vector is generated from each text and the corresponding Wikipedia categories extracted from Wikipedia articles. Input vectors are formed in the common category taking the hierarchical structure of Wikipedia category into consideration. By using the proposed method, the problem of reconfiguration of vector elements caused by dynamic changes in the text can be solved. Moreover, information loss in newly obtained index term can be prevented.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.132 No.8 （2012） 特集：光・量子ビームによるナノダイナミクス

本誌掲載ページ: 1347-1355 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/132/8/132_1347/_article/-char/ja/