DocumentCode
1629822
Title
An analysis of partition index maximization algorithm
Author
Wu, Kuo-Lung
Author_Institution
Dept. of Inf. Manage., Kun Shan Univ., Tainan, Taiwan
fYear
2009
Firstpage
1785
Lastpage
1790
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location
Jeju Island
ISSN
1098-7584
Print_ISBN
978-1-4244-3596-8
Electronic_ISBN
1098-7584
Type
conf
DOI
10.1109/FUZZY.2009.5277353
Filename
5277353
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