Patterns discovery on complex diagnosis and biological data using fuzzy latent variables

Zong Xian Yin, Jung Hsien Chiang

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

2 Citations (Scopus)

Abstract

This paper proposes a new clustering algorithm referred to as the Possibilitic Latent Variables (PLV) clustering algorithm. This algorithm provides a powerful tool for the analysis of complex data, such as clinical diagnosis and biological expressions data, due to its robustness to various data distributions and its accuracy in establishing appropriate groups from data. The algorithm combines a distribution model and the fuzzy degrees concept. Compared to the expectation-maximization (EM) algorithm, which is a well-known distribution estimating algorithm, the PLV algorithm has the considerable advantage that it can be applied to various data types, i.e. it is not restricted solely to Gaussian data distributions. Additionally, the proposed algorithm has a better performance than the well-known fuzzy clustering algorithm, i.e. the FCMalgorithm, where it can address compact regions, other than simply dividing objects into several equal populations. The performance of the proposed algorithm is verified by conducting clustering tasks on the contents of several medical diagnosis and biological expressions datasets.

Original languageEnglish
Title of host publication23rd International Conference on Data Engineering, ICDE 2007
Pages576-585
Number of pages10
DOIs
Publication statusPublished - 2007 Sep 24
Event23rd International Conference on Data Engineering, ICDE 2007 - Istanbul, Turkey
Duration: 2007 Apr 152007 Apr 20

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other23rd International Conference on Data Engineering, ICDE 2007
CountryTurkey
CityIstanbul
Period07-04-1507-04-20

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

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