Dampster-shafer evidence theory based multi-characteristics fusion for clustering evaluation

Shihong Yue, Teresa Wu, Yamin Wang, Kai Zhang, Weixia Liu

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

1 Scopus citations

Abstract

Clustering is a widely used unsupervised learning method to group data with similar characteristics. The performance of the clustering method can be in general evaluated through some validity indices. However, most validity indices are designed for the specific algorithms along with specific structure of data space. Moreover, these indices consist of a few within- and between- clustering distance functions. The applicability of these indices heavily relies on the correctness of combining these functions. In this research, we first summarize three common characteristics of any clustering evaluation: (1) the clustering outcome can be evaluated by a group of validity indices if some efficient validity indices are available, (2) the clustering outcome can be measured by an independent intra-cluster distance function and (3) the clustering outcome can be measured by the neighborhood based functions. Considering the complementary and unstable natures among the clustering evaluation, we then apply Dampster-Shafter (D-S) Evidence Theory to fuse the three characteristics to generate a new index, termed fused Multiple Characteristic Indices (fMCI). The fMCI generally is capable to evaluate clustering outcomes of arbitrary clustering methods associated with more complex structures of data space. We conduct a number of experiments to demonstrate that the fMCI is applicable to evaluate different clustering algorithms on different datasets and the fMCI can achieve more accurate and robust clustering evaluation comparing to existing indices.

Original languageEnglish (US)
Title of host publicationRough Set and Knowledge Technology - 5th International Conference, RSKT 2010, Proceedings
Pages499-519
Number of pages21
DOIs
StatePublished - Nov 22 2010
Event5th International Conference on Rough Set and Knowledge Technology, RSKT 2010 - Beijing, China
Duration: Oct 15 2010Oct 17 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6401 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Rough Set and Knowledge Technology, RSKT 2010
CountryChina
CityBeijing
Period10/15/1010/17/10

Keywords

  • Dampster-Shafer evidence theory
  • Validity index
  • clustering algorithm
  • data structure

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Yue, S., Wu, T., Wang, Y., Zhang, K., & Liu, W. (2010). Dampster-shafer evidence theory based multi-characteristics fusion for clustering evaluation. In Rough Set and Knowledge Technology - 5th International Conference, RSKT 2010, Proceedings (pp. 499-519). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6401 LNAI). https://doi.org/10.1007/978-3-642-16248-0_70