Title :
An analysis of partition index maximization algorithm
Author_Institution :
Dept. of Inf. Manage., Kun Shan Univ., Tainan, Taiwan
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.
Keywords :
fuzzy set theory; optimisation; pattern clustering; fuzzy c-means clustering algorithm; parameter selection problem; partition index maximization algorithm; Algorithm design and analysis; Clustering algorithms; Clustering methods; Euclidean distance; Iterative algorithms; Partitioning algorithms; Quantization; Robustness; Shape; Unsupervised learning;
Conference_Titel :
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location :
Jeju Island
Print_ISBN :
978-1-4244-3596-8
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2009.5277353
