Universiti Teknologi Malaysia Institutional Repository

An efficient optimization method for solving unsupervised data classification problems

Shabanzadeh, Parvaneh and Yusof, Rubiyah (2015) An efficient optimization method for solving unsupervised data classification problems. Computatiol And Mathematical Methods In Medicine, 2015 . pp. 1-9. ISSN 1748-670X

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Official URL: http://dx.doi.org/10.1155/2015/802754

Abstract

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

Item Type:Article
Uncontrolled Keywords:humans, models, statistical, phylogeography
Subjects:Q Science > Q Science (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:57749
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 04:07
Last Modified:04 Aug 2021 13:15

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