Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset

Muchamad Kurniawan, Rani Rotul Muhima, Siti Agustini

Abstract


One of the causes of forest fires is the lack of speed of handling when a fire occurs. This can be anticipated by determining how many extinguishing units are in the center of the hot spot. To get hotspots, NASA has provided an active fire dataset. The clustering method is used to get the most optimal centroid point. The clustering methods we use are K-Means, Fuzzy C-Means (FCM), and Average Linkage. The reason for using K-means is a simple method and has been applied in various areas. FCM is a partition-based clustering algorithm which is a development of the K-means method. The hierarchical based clustering method is represented by the Average Linkage method.  The measurement technique that uses is the sum of the internal distance of each cluster. Elbow evaluation is used to evaluate the optimal cluster. The results obtained after conducting the K-Means trial obtained the best results with a total distance of 145.35 km, and the best clusters from this method were 4 clusters. Meanwhile, the total distance values obtained from the FCM and Linkage methods were 154.13 km and 266.61 km.




Keywords


Active fire dataset, K-Means, FCM, Linkage, Elbow Clustering.

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DOI: http://dx.doi.org/10.25139/ijair.v2i2.3030

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