2012年10月
Clustering of functional data in a low-dimensional subspace
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
- 巻
- 6
- 号
- 3
- 開始ページ
- 219
- 終了ページ
- 247
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1007/s11634-012-0113-3
- 出版者・発行元
- SPRINGER HEIDELBERG
To find optimal clusters of functional objects in a lower-dimensional subspace of data, a sequential method called tandem analysis, is often used, though such a method is problematic. A new procedure is developed to find optimal clusters of functional objects and also find an optimal subspace for clustering, simultaneously. The method is based on the k-means criterion for functional data and seeks the subspace that is maximally informative about the clustering structure in the data. An efficient alternating least-squares algorithm is described, and the proposed method is extended to a regularized method. Analyses of artificial and real data examples demonstrate that the proposed method gives correct and interpretable results.
- リンク情報
- ID情報
-
- DOI : 10.1007/s11634-012-0113-3
- ISSN : 1862-5347
- Web of Science ID : WOS:000309344800005