システム制御情報学会論文誌
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
ファジィクラスタリングを用いた主成分分析における変量選択とデータマイニングへの応用
本多 克宏市橋 秀友
著者情報
ジャーナル フリー

2005 年 18 巻 9 号 p. 322-330

詳細
抄録

Knowledge discovery in databases (KDD) or data mining involves fitting models to or determining patterns from high dimensional data sets, and extraction of correlation rules plays an important role. This paper proposes a new approach to knowledge discovery with linear model estimation, in which principal component analysis (PCA) is performed by selecting variables. The proposed algorithm is a hybrid of fuzzy clustering and PCA based on lower rank approximation of data matrix, in which the relative responsibilities of the variables are estimated by using possibilistic constraint for memberships. The proposed algorithm is also enhanced to a local PCA model that can be used for data mining by performing both of linear model estimation and stratified sampling.

著者関連情報
© システム制御情報学会
前の記事 次の記事
feedback
Top