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
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
Citation
Sarkar M. and Tze-Yun LEONG.
Fuzzy K-means clustering with missing values. (2001). American Medical Informatics Association Annual Fall Symposium (AMIA). 588-592.
Available at: https://ink.library.smu.edu.sg/sis_research/3020
Additional URL
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2243620/