Automatic Multi-Objective Clustering Algorithm Using Hybrid Particle Swarm Optimization With Simulated Annealing.

Abubaker, Ahmad Asad (2016) Automatic Multi-Objective Clustering Algorithm Using Hybrid Particle Swarm Optimization With Simulated Annealing. PhD thesis, Universiti Sains Malaysia.

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    Abstract

    Pengelompokan adalah suatu teknik pelombongan data. Di dalam bidang set data tanpa selia, tugas mengelompok ialah dengan mengumpul set data kepada kelompok yang bermakna. Pengelompokan digunakan sebagai teknik penyelesaian di dalam pelbagai bidang dengan membahagikan dan mengstruktur semula data yang besar dan kompleks supaya menjadi lebih bererti justru mengubahnya kepada maklumat yang berguna. Clustering is a data mining technique. In the field of unsupervised datasets, the task of clustering is by grouping the dataset into meaningful clusters. Clustering is used as a data solution technique in various fields to divide and restructure the large and complex data to become more significant thus transform them into useful information.

    Item Type: Thesis (PhD)
    Subjects: Q Science > QA Mathematics > QA1 Mathematics (General)
    Divisions: Pusat Pengajian Sains Matematik (School of Mathematical Sciences) > Thesis
    Depositing User: Mr Erwan Roslan
    Date Deposited: 26 Jan 2018 10:01
    Last Modified: 17 May 2018 11:10
    URI: http://eprints.usm.my/id/eprint/38568

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