An improved clustering based on K-means for hotspots data
Creators
- 1. Department of Informatics Engineering, Faculty of Electrical Engineering and Information Technology, Institut Teknologi Adhi Tama Surabaya (ITATS), Surabaya, Indonesia
- 2. Department of Electrical Engineering, Faculty of Industrial Technology, Ahmad Dahlan University, Yogyakarta, Indonesia
- 3. Prevention Division, Regional Disaster Management Agency of Riau Province, Pekan Baru, Indonesia
Description
Riau province is one of the provinces in Indonesia where forest fires frequently occur every year. Hotspot data is geothermal points and they can be utilized as an indicator of forest fires. Clustering’s method can be used to analyze potential forest fires from hotspot data’s cluster pattern. In this study, hybrid genetic algorithm polygamy with K-means (GAP K-means) was used for hotspot data clustering. GA polygamy was used to determine the initial centroid of K-means. It was used to solve the sensitivity of K-means to the initial centroid, and to find the optimal solution faster. Experimentally compared the performance of GAP K-means, GA K-means, and K-means on the hotspots data, two artificial datasets, and three real-life datasets. Sum square error (SSE), davies bouldin index (DBI), silhouette coefficient (SC) and F-measure are used to evaluation clustering. Based this experiment, GAP K-means outperforms than K-means but GAP K-means still not fast to achieve convergent than GA K-means.
Files
31033-65424-1-PB.pdf
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