A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques

Sharifah Sakinah, Syed Ahmad (2014) A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques. In: Malaysian Technical Universities Conference on Engineering & Technology (MUCET 2014), 10-11 November 2014, Melaka.

[img] PDF
Mucet_sakinah.pdf - Accepted Version
Restricted to Registered users only

Download (607kB) | Request a copy

Download (607kB)


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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Fuzzy C-Means, K-Means, Clustering techniques, Analysis
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: DR Sharifah Sakinah Syed Ahmad
Date Deposited: 20 Jan 2015 04:28
Last Modified: 28 May 2015 04:36
URI: http://eprints.utem.edu.my/id/eprint/14073
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item