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Indexed by:期刊论文

Date of Publication:2015-07-01

Journal:ICIC Express Letters

Included Journals:EI、Scopus

Volume:9

Issue:8

Page Number:2177-2182

ISSN No.:1881803X

Abstract:Many real world datasets contain missing values, and partially missing datasets have become a prevailing problem in pattern recognition. In such problems, the rationality of missing value imputation is a key issue. In this paper, missing values are represented as nearest-prototype neighborhoods, which make an efficient use of the prototype information and can be achieved by considering fuzzy cluster covariance matrix. And then, as the nearest-prototype neighborhood representation can constrain the imputation of missing values in rational ranges rather than the entire attribute space, a novel fuzzy c-means algorithm with the neighborhood constrains is proposed, which can avoid the imputations from falling into unnecessary local minima, and enhance the rationality of both the imputation of missing values and clustering results. The proposed algorithm is evaluated with several benchmark datasets and the results demonstrate the better clustering performance of our approach over the compared methods. ? 2015 ICIC International.