Adaptive Fuzzy Clustering for improving classification performance in yeast data set

Man Sun Kim, Hyung Jeong Yang, Wooi Ping Cheah

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In data mining, there is inter-category imbalance of data which includes unnecessary data that hinder the formulation of an efficient model. This paper called FSFC+ introduces a new focused sampling based on adaptive Fuzzy Clustering. By applying FSFC+, the optimal number of clusters was used by adaptive method. It removes unuseful data that can be obstacles to the formulation of an efficient model. When there is no information about data set, we would evaluate the fitness of partitions produced by cluster validity index. In addition, it is very useful in data analysis because it can quantify the degree of membership of data to multiple clusters.

Original languageEnglish
Title of host publication2008 4th International IEEE Conference Intelligent Systems, IS 2008
Pages182-187
Number of pages6
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 4th International IEEE Conference Intelligent Systems, IS 2008 - Varna, Bulgaria
Duration: 6 Sep 20088 Sep 2008

Publication series

Name2008 4th International IEEE Conference Intelligent Systems, IS 2008
Volume2

Conference

Conference2008 4th International IEEE Conference Intelligent Systems, IS 2008
Country/TerritoryBulgaria
CityVarna
Period6/09/088/09/08

Keywords

  • Adaptive Fuzzy Clustering
  • Cluster validity
  • Focused sampling
  • Selective sampling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Electrical and Electronic Engineering

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