University of Hertfordshire

A clustering based approach to reduce feature redundancy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Documents

  • Renato Cordeiro De Amorim
  • Boris Mirkin
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Original languageEnglish
Title of host publicationKnowledge, Information and Creativity Support Systems
Subtitle of host publicationRecent Trends, Advances and Solutions
PublisherSpringer
Pages465-475
Number of pages11
Volume364
ISBN (Electronic)978-3-319-19090-7
ISBN (Print)978-3-319-19089-1
DOIs
Publication statusPublished - 26 Feb 2016
EventKnowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions - Krakow, Poland
Duration: 7 Nov 20139 Nov 2013
http://www.kicss2013.ipbf.eu/

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer

Conference

ConferenceKnowledge, Information and Creativity Support Systems
Abbreviated titleKICSS'2013
CountryPoland
CityKrakow
Period7/11/139/11/13
Internet address

Abstract

Research effort has recently focused on designing feature weighting clustering algorithms. These algorithms automatically calculate the weight of each feature, representing their degree of relevance, in a data set. However, since most of these evaluate one feature at a time they may have difficulties to cluster data sets containing features with similar information. If a group of features contain the same relevant information, these clustering algorithms set high weights to each feature in this group, instead of removing some because of their redundant nature. This paper introduces an unsupervised feature selection method that can be used in the data pre-processing step to reduce the number of redundant features in a data set. This method clusters similar features together and then selects a subset of representative features for each cluster. This selection is based on the maximum information compression index between each feature and its respective cluster centroid. We present an empirical validation for our method by comparing it with a popular unsupervised feature selection on three EEG data sets. We find that our method selects features that produce better cluster recovery, without the need for an extra user-defined parameter.

Notes

This document is the Accepted Manuscript version of the following paper: Cordeiro de Amorim, R.,and Mirkin, B., ‘A clustering based approach to reduce feature redundancy’, in Proceedings, Andrzej M. J. Skulimowski and Janusz Kacprzyk, eds., Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions, Selected papers from KICSS’2013 - 8th International Conference on Knowledge, Information, and Creativity Support Systems, Kraków, Poland, 7-9 November 2013. ISBN 978-3-319-19089-1, e-ISBN 978-3-319-19090-7. Available online at doi: 10.1007/978-3-319-19090-7. © Springer International Publishing Switzerland 2016.

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