Pendekatan Initial Centroid Search Untuk Meningkatkan Efisiensi Iterasi Klustering K-Means
DOI:
https://doi.org/10.33633/tc.v19i4.3875Keywords:
Titik Pusat Cluster, Pengelompokkan, Sum of Square Error, Konvergen, K-MeansAbstract
Pengelompokan K-Means bertujuan untuk mengumpulkan satu set titik pusat cluster yang optimal melalui iterasi yang berurutan. Fakta bahwa semakin optimal posisi dari titik pusat awal maka semakin sedikit jumlah iterasi dari algoritma pengelompokkan K-Means untuk konvergen. Oleh karena itu, Salah satu cara untuk menemukan set initial centroid adalah melalui metode iteratif guna mencari sejumlah initial centroid yang lebih baik untuk proses pengelompokan K-Means. Langkah awal yang kami lakukan adalah mengambil sampel data dari set data dan menjalankan algoritma K-Means sebagai proses awal untuk inisialisasi centroid cluster. Kemudian kami mengulang proses iterasi dengan sejumlah initial centroid yang telah diinisialisasikan sebelumnya dan mengukur hasil pengelompokkan melalui sum-of-square-error guna menentukan kebaikan keanggotaan cluster. Centroid akhir yang memberikan jarak terendah yang akan kami teruskan ke proses pengelompokan K-means secara lengkap. Harapan kami adalah pendekatan ini akan mengarah pada set initial centroid yang lebih baik sebagai proses pengelompokan K-Means sehingga mampu meningkatkan kinerja Algoritma K-Means karena hasil konvergensi Algoritma K-Means akan berbanding lurus dengan pemilihan initial centroid.References
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