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.
|Title of host publication||Biologically-Inspired Techniques for Knowledge Discovery and Data Mining|
|Editors||Shafiq Alam, Gillian Dobbie, Yun Sing Koh, Saeed ur Rehman|
|Place of Publication||Hershy, PA, United States|
|Number of pages||10|
|ISBN (Print)||1466660783, 9781466660786|
|Publication status||Published - 31 May 2014|