Title

Fuzzy K-means clustering with missing values

Publication Type

Conference Proceeding Article

Publication Date

1-2001

Abstract

Fuzzy K-means clustering algorithm is a popular approach for exploring the structure of a set of patterns, especially when the clusters are overlapping or fuzzy. However, the fuzzy K-means clustering algorithm cannot be applied when the real-life data contain missing values. In many cases, the number of patterns with missing values is so large that if these patterns are removed, then sufficient number of patterns is not available to characterize the data set. This paper proposes a technique to exploit the information provided by the patterns with the missing values so that the clustering results are enhanced. There are various preprocessing methods to substitute the missing values before clustering the data. However, instead of repairing the data set at the beginning, the repairing can be carried out incrementally in each iteration based on the context. In that case, it is more likely that less uncertainty is added while incorporating the repair work. This scheme is further consolidated in this paper by fine-tuning the missing values using the information from other attributes. The applications of the proposed method in medical domain have produced good performance.

Discipline

Computer Sciences | Health Information Technology

Research Areas

Intelligent Systems and Decision Analytics

Publication

American Medical Informatics Association Annual Fall Symposium (AMIA)

First Page

588

Last Page

592

ISBN

1091-8280

Publisher

Hanley & Belfus

City or Country

Washington DC, USA

Additional URL

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2243620/

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