Optimasi Algoritma Fuzzy Clustering dengan Menggunakan Algoritma Forest Optimization

Penulis

  • Robert Kurniawan Politeknik Statistika STIS Jakarta http://orcid.org/0000-0002-8275-4070
  • Edhi Prabowo Badan Pusat Statistik Kabupaten Simeulue, Aceh

Abstrak

Fuzzy C-Means (FCM) adalah salah satu teknik clustering yang sering digunakan, tetapi memiliki kelemahan yaitu sensitif terhadap local optima dan sensitif terhadap pusat cluster awal. Forest Optimization Algortihm mampu mengatasi kelemahan dari FCM. FOFCM dibangun dengan 2 jenis jarak yaitu Euclidean dan Mahalanobis. FOFCM memiliki performa yang lebih baik dari FCM, karena sebagian besar iterasi FOFCM lebih sedikit dari FCM. FOFCM Mahalanobis menghasilkan nilai fungsi objektif paling kecil pada sebaran data hyperspherical dibandingkan dengan FOFCM Euclidean maupun FCM. Oleh karena itu, dapat disimpulkan bahwa FOFCM Mahalanobis cocok untuk data hyperspherical.

Biografi Penulis

Robert Kurniawan, Politeknik Statistika STIS Jakarta

Statistika Komputasi

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Diterbitkan

2019-01-24