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ファイル | 記述 | サイズ | フォーマット | |
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j.csda.2014.05.010.pdf | 851.21 kB | Adobe PDF | 見る/開く |
タイトル: | Functional factorial K-means analysis |
著者: | Yamamoto, Michio Terada, Yoshikazu |
著者名の別形: | 山本, 倫生 |
キーワード: | Functional data Cluster analysis Dimension reduction Tandem analysis K-means algorithm |
発行日: | Nov-2014 |
出版者: | Elsevier B.V. |
誌名: | Computational Statistics & Data Analysis |
巻: | 79 |
開始ページ: | 133 |
終了ページ: | 148 |
抄録: | A new procedure for simultaneously finding the optimal cluster structure of multivariate functional objects and finding the subspace to represent the cluster structure is presented. The method is based on the k-means criterion for projected functional objects on a subspace in which a cluster structure exists. An efficient alternating least-squares algorithm is described, and the proposed method is extended to a regularized method for smoothness of weight functions. To deal with the negative effect of the correlation of the coefficient matrix of the basis function expansion in the proposed algorithm, a two-step approach to the proposed method is also described. Analyses of artificial and real data demonstrate that the proposed method gives correct and interpretable results compared with existing methods, the functional principal component k-means (FPCK) method and tandem clustering approach. It is also shown that the proposed method can be considered complementary to FPCK. |
著作権等: | © 2014 Elsevier B.V. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 This is not the published version. Please cite only the published version. |
URI: | http://hdl.handle.net/2433/188944 |
DOI(出版社版): | 10.1016/j.csda.2014.05.010 |
出現コレクション: | 学術雑誌掲載論文等 |
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