Biologically Inspired Techniques for Data Mining: A Brief Overview of Particle Swarm Optimization for KDD

Biologically Inspired Techniques for Data Mining: A Brief Overview of Particle Swarm Optimization for KDD

Shafiq Alam (University of Auckland, New Zealand), Gillian Dobbie (The University of Auckland, New Zealand), Yun Sing Koh (The University of Auckland, New Zealand) and Saeed ur Rehman (Unitec Institute of Technology, New Zealand)
DOI: 10.4018/978-1-4666-9562-7.ch112
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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.
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Swarm Intelligence

Swarm Intelligence, inspired by the biological behavior of animals, birds, and fish, is an innovative intelligent optimization technique (Abraham et al., 2006) (Engelbrecht, 2006). SI techniques are based on the collective behavior of swarms of bees, fish schools, and colonies of insects while searching for food, communicating with each other and socializing in their colonies. The SI models are based on self-organization, decentralization, communication, and cooperation between the individuals within the team. The individual interaction is very simple but emerges as a complex global behavior, which is the core of swarm intelligence (Bonabeau & Meyer, 2001). Although swarm intelligence based techniques have primarily been used and found very efficient in traditional optimization problems, a huge growth in these techniques has been observed in other areas of research. These application areas vary from optimizing the solution for planning, scheduling, resource management, and network optimization problems. Data mining is one of the contemporary areas of application, where these techniques have been found to be efficient for clustering, classification, feature selection and outlier detection. The use of swarm intelligence has been extended from conventional optimization problems to optimization-based data mining.

A number of SI based techniques with many variants have been proposed in the last decade and the number of new techniques is growing. Among different SI techniques, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are the two main techniques, which are widely used for solving discrete and continuous optimization problems. In the next sections we will discuss the foundation PSO followed by its use in KDD.

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