A possibilistic density based clustering for discovering clusters of arbitrary shapes and densities in high dimensional data

Noha Yousri, Mohamed S. Kamel, Mohamed A. Ismail

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

1 Citation (Scopus)

Abstract

Apart from the interesting problem of finding arbitrary shaped clusters of different densities, some applications further introduce the challenge of finding overlapping clusters in the presence of outliers. Fuzzy and possibilistic clustering approaches have therefore been developed to handle such problem, where possibilistic clustering is able to handle the presence of outliers compared to its fuzzy counterpart. However, current known fuzzy and possibilistic algorithms are still inefficient to use for finding the natural cluster structure. In this work, a novel possibilistic density based clustering approach is introduced, to identify the degrees of typicality of patterns to clusters of arbitrary shapes and densities. Experimental results illustrate the efficiency of the proposed approach compared to related algorithms.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages577-584
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 19 Nov 2012
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7665 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Neural Information Processing, ICONIP 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

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Keywords

  • Arbitrary densities
  • Arbitrary Shapes
  • Possibilistic Clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yousri, N., Kamel, M. S., & Ismail, M. A. (2012). A possibilistic density based clustering for discovering clusters of arbitrary shapes and densities in high dimensional data. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings (PART 3 ed., pp. 577-584). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7665 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-34487-9_70