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An Efficient Optimization Method for Solving Unsupervised Data Classification Problems
Joint Authors
Shabanzadeh, Parvaneh
Yusof, Rubiyah
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-29
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Shabanzadeh, Parvaneh& Yusof, Rubiyah. 2015. An Efficient Optimization Method for Solving Unsupervised Data Classification Problems. Computational and Mathematical Methods in Medicine،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1058002
Modern Language Association (MLA)
Shabanzadeh, Parvaneh& Yusof, Rubiyah. An Efficient Optimization Method for Solving Unsupervised Data Classification Problems. Computational and Mathematical Methods in Medicine No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1058002
American Medical Association (AMA)
Shabanzadeh, Parvaneh& Yusof, Rubiyah. An Efficient Optimization Method for Solving Unsupervised Data Classification Problems. Computational and Mathematical Methods in Medicine. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1058002
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1058002