Multi-layer topology preserving mapping for K-means clustering

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Abstract

In this paper, we investigate the multi-layer topology preserving mapping for K-means. We present a Multi-layer Topology Preserving Mapping (MTPM) based on the idea of deep architectures. We demonstrate that the MTPM output can be used to discover the number of clusters for K-means and initialize the prototypes of K-means more reasonably. Also, K-means clusters the data based on the discovered underlying structure of the data by the MTPM. The standard wine data set is used to test our algorithm. We finally analyse a real biological data set with no prior clustering information available.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings
Pages84-91
Number of pages8
DOIs
Publication statusPublished - 2011
Event12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011 - Norwich, United Kingdom
Duration: 7 Sep 20119 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6936 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011
Country/TerritoryUnited Kingdom
CityNorwich
Period7/09/119/09/11

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