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Aplikasi algoritma metaheuristik basis fuzzy K- modes untuk supplier clustering = Application of metaheuristic based fuzzy K-modes algorithm to supplier clustering / Yuliana Portti

Yuliana Portti; Amalia Suzianti, supervisor; Arian Dhini, supervisor; Erlinda Muslim, examiner; Maya Arlini Puspasari, examiner ([Publisher not identified] , 2015)

 Abstrak

[ABSTRAK
Penelitian ini mengusulkan tiga algoritma meta-heuristik berbasis Fuzzy K-modes
untuk clustering binary data set. Ada tiga metode metaheuristik diterapkan, yaitu
Particle Swarm Optimization (PSO), Genetika Algoritma (GA), dan Artificial Bee
Colony (ABC). Ketiga algoritma digabungkan dengan algoritma K-modes.
Tujuannya adalah untuk memberikan modes awal yang lebih baik untuk K-modes.
Jarak antara data ke modes dihitung dengan menggunakan koefisien Jaccard.
Koefisien Jaccard diterapkan karena dataset mengandung banyak nilai nol . Dalam
rangka untuk melakukan pengelompokan set data real tentang supplier otomotif di
Taiwan, algoritma yang diusulkan diverifikasi menggunakan benchmark set data.
Hasil penelitian menunjukkan bahwa PSO K-modes dan GA K-modes lebih baik
dari ABC K-modes. Selain itu, dari hasil studi kasus, GA K-modes memberikan
SSE terkecil dan juga memiliki waktu komputasi lebih cepat dari PSO K-modes
dan ABC K-modes.
ABSTRACT
This study proposed three meta-heuristic based fuzzy K-modes algorithms for
clustering binary dataset. There are three meta-heuristic methods applied, namely
Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA) algorithm,
and Artificial Bee Colony (ABC) algorithm. These three algorithms are combined
with k-modes algorithm. Their aim is to give better initial modes for the k-modes.
Herein, the similarity between two instances is calculated using jaccard coefficient.
The Jaccard coefficient is applied since the dataset contains many zero values. In
order to cluster a real data set about automobile suppliers in Taiwan, the proposed
algorithms are verified using benchmark data set. The experiments results show
that PSO K-modes and GA K-modes is better than ABC K-modes. Moreover,
from case study results, GA fuzzy K-modes gives the smallest SSE and also has
faster computational time than PSO fuzzy K-modes and ABC fuzzy K-modes., This study proposed three meta-heuristic based fuzzy K-modes algorithms for
clustering binary dataset. There are three meta-heuristic methods applied, namely
Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA) algorithm,
and Artificial Bee Colony (ABC) algorithm. These three algorithms are combined
with k-modes algorithm. Their aim is to give better initial modes for the k-modes.
Herein, the similarity between two instances is calculated using jaccard coefficient.
The Jaccard coefficient is applied since the dataset contains many zero values. In
order to cluster a real data set about automobile suppliers in Taiwan, the proposed
algorithms are verified using benchmark data set. The experiments results show
that PSO K-modes and GA K-modes is better than ABC K-modes. Moreover,
from case study results, GA fuzzy K-modes gives the smallest SSE and also has
faster computational time than PSO fuzzy K-modes and ABC fuzzy K-modes.]

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No. Panggil : T44406
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Entri tambahan-Nama badan :
Subjek :
Penerbitan : [Place of publication not identified]: [Publisher not identified], 2015
Program Studi :
Bahasa : ind
Sumber Pengatalogan : LibUI ind rda
Tipe Konten : text
Tipe Media : unmediated ; computer
Tipe Carrier : volume ; online resource
Deskripsi Fisik : xv, 93 pages : illustration ; 28 cm + appendix
Naskah Ringkas :
Lembaga Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI, Lantai 3
  • Ketersediaan
  • Ulasan
No. Panggil No. Barkod Ketersediaan
T44406 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 20414356
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