Comparison of Industrial Business Grouping Using Fuzzy C-Means and Fuzzy Possibilistic C-Means Methods

Mega Lestari, Dwi Kartini, Irwan Budiman, Mohammad Reza Faisal, Muliadi Muliadi


The industrial business sector plays a role in the development of the economic sector in developing countries such as Indonesia. In this case, many industrial businesses are growing, but the data has not been processed or analyzed to produce important information that can be processed into knowledge using data mining. One of the data mining techniques used in this research is data grouping, or clustering. This research was conducted to determine the comparison results of the Cluster Validity Index on Fuzzy C-Means and Fuzzy Possibilistic C-Means methods for clustering industrial businesses in Tanah Bumbu Regency. In each process, 5 trials were conducted with the number of clusters, namely 3, 4, 5, 6, and 7, and for the attributes used: Male Labor, Female Labor, Investment Value, Production Value, and BW/BP Value. Furthermore, this study will evaluate the Cluster Validity Index, namely the Partition Entropy Index, Partition Coefficient index, and Modified Partition Coefficient Index. This research provides the best performance results in the Fuzzy C-Means method with the results of the Cluster Validity Index on the Partition Entropy Index of 0.21566, Partition Coefficient Index of 0.88078, and Modified Partition Coefficient Index of 0.82117, and the best number of clusters is 3 with the labels of low competitive industry clusters, medium competitive industry clusters, and highly competitive industry clusters.


Industrial Business; Clustering; Fuzzy C-Means; Fuzzy Possibilistic C-Means; Cluster Validity Index

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ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
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