Original title: Robust Regularized Cluster Analysis for High-Dimensional Data
Authors: Kalina, Jan ; Vlčková, Katarína
Document type: Papers
Conference/Event: MME 2014. International Conference Mathematical Methods in Economics /32./, Olomouc (CZ), 2014-09-10 / 2014-09-12
Year: 2014
Language: eng
Abstract: This paper presents new approaches to the hierarchical agglomerative cluster analysis for high-dimensional data. First, we propose a regularized version of the hierarchical cluster analysis for categorical data with a large number of categories. It exploits a regularized version of various test statistics of homogeneity in contingency tables as the measure of distance between two clusters. Further, our aim is cluster analysis of continuous data with a large number of variables. Various regularization techniques tailor-made for high-dimensional data have been proposed, which have however turned out to suffer from a high sensitivity to the presence of outlying measurements in the data. As a robust solution, we recommend to combine two newly proposed methods, namely a regularized version of robust principal component analysis and a regularized Mahalanobis distance, which is based on an asymptotically optimal regularization of the covariance matrix. We bring arguments in favor of the newly proposed methods.
Keywords: big data; cluster analysis; regularization; robust data mining
Project no.: GA13-17187S (CEP), GA13-01930S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: Proceedings of 32nd International Conference Mathematical Methods in Economics MME 2014, ISBN 978-80-244-4209-9

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0236247

Permalink: http://www.nusl.cz/ntk/nusl-175211


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