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Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms
Simon Fong
2013-08-29
Source PublicationSwarm Intelligence and Bio-Inspired Computation: Theory and Applications
PublisherELSEVIER SCIENCE BV, SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Pages385-402
Abstract

Data mining has evolved from methods of simple statistical analysis to complex pattern recognition in the past decades. During the progression, the data mining algorithms are modified or extended in order to overcome some specific problems. This chapter discusses about the prospects of improving data mining algorithms by integrating bio-inspired optimization, which has lately captivated much of researchers' attention. In particular, high dimensionality and the unavailability of the whole data set (as in stream mining) in the training data have known to be two major challenges. We demonstrated that these two challenges, through two small examples such as K-means clustering and time-series classification, can be overcome by integrating data mining and bio-inspired algorithms. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.

KeywordBio-inspired Classification Clustering Data Mining Feature Selection Optimization
DOIhttps://doi.org/10.1016/B978-0-12-405163-8.00018-1
URLView the original
Language英语
ISBN978-0-12-405177-5; 978-0-12-405163-8
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Document TypeBook chapter
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science, University of Macau, Macau SAR, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms:ELSEVIER SCIENCE BV, SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS,2013:385-402.
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