Biologically inspired techniques for data mining: A brief overview of particle swarm optimization for KDD

Shafiq Alam, Gillian Dobbie, Yun Sing Koh, Saeed Ur Rehman

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Knowledge 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 techniques.

Original languageEnglish
Title of host publicationBiologically-Inspired Techniques for Knowledge Discovery and Data Mining
EditorsShafiq Alam, Gillian Dobbie, Yun Sing Koh, Saeed ur Rehman
Place of PublicationHershy, PA, United States
PublisherIGI Global
Chapter1
Pages1-10
Number of pages10
ISBN (Electronic)9781466660793
ISBN (Print)1466660783, 9781466660786
DOIs
Publication statusPublished - 31 May 2014
Externally publishedYes

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