ANALISIS CLUSTER OTOMATIS MENGGUNAKAN ALGORITMA NOVEL MODIFIED DIFFERENTIAL EVOLUTION
Abstract
Analisis cluster merupakan salah satu permasalahan pembelajaran tidak terbimbing dan teknik datamining yang penting. Akan tetapi, untuk menentukan jumlah cluster akhir merupakan suatu tugasyang menantang. Oleh karena itu, penelitian ini bermaksud mengusulkan algoritma novel modifieddifferential evolution (NMDE) dan algoritma k-means (NMDE-k-means) pada analisis clusterotomatis. Algoritma ini dapat menentukan jumlah cluster akhir dan melakukan pengelompokandata secara otomatis. Pada prinsipnya Algoritma NMDE akan melakukan pencarian global untukmenemukan jumlah cluster dan partisi data, sedangkan algoritma k-means akan memperbaikikinerja algoritma NMDE dalam menentukan centroid cluster. Empat data set yang sudah dikenalyaitu Iris, Wine, Glass dan Vowel digunakan untuk memvalidasi algoritma ini. Hasil komputasimenunjukkan bahwa algoritma ini lebih baik dibandingkan dengan empat algoritma clusterotomatis lainnya yaitu improved automatic clustering based differential evolution (ACDE),automatic clustering using differential evolutionandk-means (ACDE-k-means) dan algoritmacluster otomatis yang berbasis particle swarm optimization (PSO) serta genetic algorithm (GA)References
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