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Journal > Jurnal Kursor > ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS

 

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Jurnal Kursor
Vol 6, No 3 (2012)
ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS
Article Info   ABSTRACT
Published date:
22 Nov 2016
 
ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS a I Made Widiartha, b Agus Zainal Arifin, c Anny Yuniarti aJurusan Ilmu Komputer, FMIPA, Universitas Udayana Kampus Bukit, Gedung BJ Lt.I, Jimbaran Bali, b,c Informatics Department, Faculty of Information Technology Institute of Technology Sepuluh Nopember E-Mail: aimdewidiartha@cs.unud.ac.id Abstrak Berbagai metode telah dibuat untuk dapat melakukan klasterisasi data. Salah satu metode tersebut adalah K-Harmonic Means Clustering (KHM). KHM merupakan metode klasterisasi data yang menyempurnakan K-Means Clustering (KM). Metode KHM telah mampu mengurangi permasalahan KM dalam hal sensitifitas pada inisialisasi titik pusat awal, meskipun demikian dalam KHM masih terdapat kemungkinan solusi yang dihasilkan merupakan suatu lokal optimal. Permasalahan lokal optimal ini dapat diatasi dengan memanfaatkan suatu metode yang memiliki karakteristik pencarian solusi global ke dalam metode KHM. Artificial Bee Colony (ABC) merupakan suatu metode swarm yang berbasis pada perilaku mencari makan dari koloni lebah madu yang memiliki karakteristik untuk menghindari kemungkinan konvergensi terhadap lokal optimal. Dalam penelitian ini diusulkan sebuah metode baru untuk klasterisasi data yang berbasis pada metode ABC dan KHM (ABC-KHM). Kinerja metode ABC-KHM ini telah dibandingkan dengan metode KHM dan ABC dengan memanfaatkan lima dataset. Dari hasil penelitian didapatkan hasil dimana metode ABC-KHM ini telah berhasil mengoptimalkan posisi titik pusat klaster KHM yang mengarahkan hasil klaster menuju suatu solusi global. Kata kunci: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM. Abstract Various methods have been made to cluster the data. One such method is K-Harmonic Means Clustering (KHM). KHM is a clustering method that improves K-Means Clustering (KM). KHM method was able to reduce the problem of KM in terms of sensitivity to the initialization of the initial center point nevertheless there is still a possibility that the result of KHM is a local optimum. The local optimal problem can be solved by utilizing a method that has characteristic of a global search into KHM method. Artificial Bee Colony (ABC) is a swarm method based on foraging behavior of honey bee colony that has characteristics to avoid the possibility of local optimum convergence. In this research, a new method for data clustering based on ABC and KHM (ABC-KHM) is proposed. The performance ABC-KHM method has been compared with ABC and KHM by using five datasets. The results show that ABC- KHM method is able to optimize the position of the cluster center and directs the center to a global solution. Key words: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM
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