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dc.contributor.authorShafiq, Alam
dc.contributor.authorGillian, Dobbie
dc.contributor.authorKoh, Yun Sing
dc.contributor.authorRehman, Saeed
dc.date.accessioned2015-08-04T01:00:56Z
dc.date.available2015-08-04T01:00:56Z
dc.date.issued2014-02-02
dc.identifier.urihttp://hdl.handle.net/10652/2959
dc.description.abstractKnowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniquesen_NZ
dc.language.isoenen_NZ
dc.publisherInformation Science Referenceen_NZ
dc.relation.urihttp://www.igi-global.com/chapter/biologically-inspired-techniques-for-data-mining/110452en_NZ
dc.relation.urihttp://www.irma-international.org/chapter/biologically-inspired-techniques-for-data-mining/110452/en_NZ
dc.rightsCopyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.en_NZ
dc.subjectdata miningen_NZ
dc.subjectdatabasesen_NZ
dc.subjectparticle swarm optimizationen_NZ
dc.subjectinformation retrievalen_NZ
dc.subjectlibrary and information scienceen_NZ
dc.titleBiologically Inspired Techniques for Data Mining: A Brief Overview of Particle Swarm Optimization for KDD. Alamen_NZ
dc.typeOtheren_NZ
dc.rights.holderIGI Globalen_NZ
dc.identifier.doiDOI: 10.4018/978-1-4666-6078-6.ch001en_NZ
dc.subject.marsden080109 Pattern Recognition and Data Miningen_NZ
dc.subject.marsden080704 Information Retrieval and Web Searchen_NZ
dc.identifier.bibliographicCitationAlam, S., Gillian, D., Koh , Y.S., and Rehman, S.U. (2014). Biologically Inspired Techniques for Data Mining: A Brief Overview of Particle Swarm Optimization for KDD. Alam. In Alam, S., Dobbie, G., Koh, Y. S., and Rehman, S. U. (Eds.), Biologically-Inspired Techniques for Knowledge Discovery and Data Mining(Eds.), (p. 1-10). Hershey, PA: Information Science Reference. doi:10.4018/978-1-4666-6078-6.ch001. NOTE: PARTIAL EXTRACT FROM CHAPTERen_NZ
unitec.institutionUniversity of Aucklanden_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.publication.spage1en_NZ
unitec.publication.lpage10en_NZ
unitec.publication.titleBiologically-Inspired Techniques for Knowledge Discovery and Data Miningen_NZ
unitec.peerreviewedyesen_NZ
unitec.identifier.roms56428en_NZ


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