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Fuzzy classification of incomplete data with adaptive volume

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

2 Scopus citations

Abstract

For solving the incomplete data problem of missing feature values in prototype data, various strategies were proposed. In this paper, two improved approaches are proposed to estimate the missing values of incomplete data. The two approaches are based on combining the adaptive volume Gustafson- Kessel algorithm (GKA) and the nearest vector features under the distance norm evaluated by complete data. The GKA with adaptive volume is applied for clustering and classifying the results. At last, compared the result with other strategies, and the computer simulations show that the improved strategies provide superior effects.

Original languageEnglish
Title of host publication2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009
Pages232-237
Number of pages6
DOIs
StatePublished - 2009
Event2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009 - Dong Hoi, Viet Nam
Duration: 1 Apr 20093 Apr 2009

Publication series

NameProceedings - 2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009

Conference

Conference2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009
Country/TerritoryViet Nam
CityDong Hoi
Period1/04/093/04/09

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