Biologically Inspired Techniques for Data Mining: A Brief Overview of Particle Swarm Optimization for KDD. Alam
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Authors
Shafiq, Alam
Gillian, Dobbie
Koh, Yun Sing
Rehman, Saeed
Gillian, Dobbie
Koh, Yun Sing
Rehman, Saeed
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2014-02-02
Supervisors
Type
Other
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
data mining
databases
particle swarm optimization
information retrieval
library and information science
databases
particle swarm optimization
information retrieval
library and information science
ANZSRC Field of Research Code (2020)
Citation
Alam, 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 CHAPTER
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
Publisher
Information Science Reference
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Link to ePress publication
DOI
DOI: 10.4018/978-1-4666-6078-6.ch001
Copyright holder
IGI Global
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