Abstract
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.
Original language | English |
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Pages (from-to) | 219-247 |
Number of pages | 29 |
Journal | Advances in Data Analysis and Classification |
Volume | 6 |
Issue number | 3 |
DOIs | |
Publication status | Published - Oct 2012 |
Externally published | Yes |
Keywords
- Clustering
- Dimension reduction
- Functional data
- Low-dimensional space
- Smoothing
ASJC Scopus subject areas
- Statistics and Probability
- Computer Science Applications
- Applied Mathematics