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PENGARUH METODE PARTICLE SWARM OPTIMIZATION DALAM PENENTUAN CENTROID AWAL TERHADAP KUALITAS HASIL CLUSTERING ALGORITMA K-MEANS

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Penilaian

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The quality of clustering results using k-means depend on initialization of early centroid. Initialization of early centroid generated randomly, produce in a convergen condition such a local optimum, therefore it needs to be found that k-means algorithm be able to produce in a convergen condition such a global optimum using Particle Swarm Optimization. In addition, most of clustering algorithm works well in handling low dimentional data and low dimentions can be achieved by doing dimentional reduction. From this research, the determination of early centroid using Particle Swarm Optimization can improve the quality of clustering results using k-means compared to the determination of early centroid randomly without dimentional reduction by showing a significant decrease in the DBI value of 31,61818134108936%. Meanwhile, the determination of early centroid using Particle Swarm Optimization can improve the quality of clustering results using k-means compared to determination of early centroid radomly with dimentional reduction by showing significant decrease in the DBI value of 38,5403727707036%

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2007000063	T34317	T343172020	Central Library (REFERENSI)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T343172020

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komunikasi., 2020

Collation

xxiv, VI-2, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.12

Content Type

Text

Media Type

other

Carrier Type

-

Edition

-

Subject(s)

Jurusan Teknik Informatika
Pemograman Aplikasi

Specific Detail Info

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Statement of Responsibility

NO

Other version/related

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