Perrbandingan Validitas Fuzzy Clustering pada Fuzzy C - Means dan Particle Swarms Optimazation (PSO) pada Pengelompokan Kelas
DOI:
https://doi.org/10.14421/jiska.2019.41-03Abstract
Clustering adalah metode yang membagi objek data ke dalam kelompok berdasarkan informasi yang ditemukan dalam data yang menggambarkan objek dan hubungan di antara mereka. Dalam analis cluster berbasis partisi metode K-Means dan Metode Fuzzy C-Means yang merupakan metode clustering yang sering dan lazim banyak digunakan masih banyak kelemahan. Dalam beberapa tahun terakhir, Particle Swarm Optimization (PSO) telah berhasil diterapkan untuk sejumlah masalah pengelompokan dunia nyata dengan konvergensi cepat dan efektif untuk data dimensi tinggi. Pengukuran yang dilakukan untuk kualitas clustering dengan fuzzy haruslah diukur dengan validitas cluster yang tepat dan sesuai dengan kriterianya masing – masing. Pengukuran perbandingan yang sangat sesuai dengan fuzzy clustering yaitu partition coefficient (PC),classification entropy (CE),Partition Index (PI),Fukuyama Sugeno Index (FS), Xie Beni Index (XBI),Modified Partition Coefficient (MPC),Partition Coefficient and Exponential Sparation (PCAES) Index.References
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