A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques
Clustering analysis has been considered as a useful means for identifying patterns in dataset. The aim for this paper is to propose a comparison study between two well-known clustering algorithms namely fuzzy c-means (FCM) and k-means. First we present an overview of both methods with emphasis on th...
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| Format: | Conference or Workshop Item |
| Language: | en en |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/14073/1/Mucet_sakinah.pdf http://eprints.utem.edu.my/id/eprint/14073/2/Mucet_sakinah.pdf http://eprints.utem.edu.my/id/eprint/14073/ |
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| Summary: | Clustering analysis has been considered as a useful means for identifying patterns in dataset. The aim for this paper is to propose a comparison study between two well-known clustering algorithms namely fuzzy c-means (FCM) and k-means. First we present an overview of both methods with emphasis on the implementation of the algorithm. Then, we apply six datasets to measure the quality of clustering result based on the similarity measure used in the algorithm and its representation of clustering result. Next, we also optimize the fuzzification variable, m in FCM algorithm in order to improve the clustering performance. Finally we compare the performance of the experimental result for both methods |
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