Share Email Print
cover

Proceedings Paper

Fuzzy c-means clustering of partially missing data sets
Author(s): Richard J. Hathaway; Dessa D. Overstreet; James C. Bezdek
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The fuzzy c-means algorithm is a useful tool for clustering real s-dimensional data. Typically, each observation consists of numerical values for s feature such as height, length, etc. In some cases, data sets contain vectors that are missing one or more feature values. For example, a particular datum might have the form: (254.3, x, 36.2, 112.7, x), where the second and fifth feature values are missing. The (standard) fuzzy c-means algorithm cannot be applied in this case since the required computations reference numerical features values for all s features of every data point. Two adaptations of fuzzy c-means to the incomplete data case are presented here. One adaptation replaces unknown feature values with additional variables that are optimized to prove an extrapolated data set yielding the smallest possible value of the fuzzy c-means criterion. Another approach uses only the available feature values in distance calculations, and then adjusts for the missing feature values by an appropriately chosen scaling of the computed distances. Numerical convergence properties of the adaptations and computational costs are discussed. Artificial data sets are used to demonstrate the two new approaches.

Paper Details

Date Published: 30 March 2000
PDF: 7 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380568
Show Author Affiliations
Richard J. Hathaway, Georgia Southern Univ. (United States)
Dessa D. Overstreet, Equifax (United States)
James C. Bezdek, Univ. of West Florida (United States)


Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

© SPIE. Terms of Use
Back to Top