Abstract:
In the traditional fuzzy c-means clustering algorithm, nearly no data points have a membership value one. Oumlzdemir and Akarum proposed a partition index maximization (P...Show MoreMetadata
Abstract:
In the traditional fuzzy c-means clustering algorithm, nearly no data points have a membership value one. Oumlzdemir and Akarum proposed a partition index maximization (PIM) algorithm which allows the data points can whole belonging to one cluster. This modification can form a core for each cluster and data points inside the core will have membership value {0,1}. In this paper, we will discuss the parameter selection problems and robust properties of the PIM algorithm.
Published in: 2009 IEEE International Conference on Fuzzy Systems
Date of Conference: 20-24 August 2009
Date Added to IEEE Xplore: 02 October 2009
ISBN Information:
Print ISSN: 1098-7584