Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/16485
Title: Two-Step-SDP approach to clustering and dimensionality reduction
Author: Macedo, Eloísa
Keywords: Data Mining
Clustering
PCA
Semidefinite Programming
Issue Date: Sep-2015
Publisher: International Academic Press
Abstract: Inspired by the recently proposed statistical technique called clustering and disjoint principal component analysis (CDPCA), this paper presents a new algorithm for clustering objects and dimensionality reduction, based on Semidefinite Programming (SDP) models. The Two-Step-SDP algorithm is based on SDP relaxations of two clustering problems and on a K-means step in a reduced space. The Two-Step-SDP algorithm was implemented and tested in R, a widely used open source software. Besides returning clusters of both objects and attributes, the Two-Step-SDP algorithm returns the variance explained by each component and the component loadings. The numerical experiments on different data sets show that the algorithm is quite efficient and fast. Comparing to other known iterative algorithms for clustering, namely, the K-means and ALS algorithms, the computational time of the Two-Step-SDP algorithm is comparable to the K-means algorithm, and it is faster than the ALS algorithm.
Peer review: yes
URI: http://hdl.handle.net/10773/16485
ISSN: 2310-5070
Publisher Version: http://www.iapress.org/index.php/soic/article/view/145
Appears in Collections:CIDMA - Artigos
OGTCG - Artigos

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