A type of unsupervised neural network used for finding patterns in input data without human intervention, consisting of Vector Quantization, Self-Organizing Map, and Learning Vector Quantization.
Published in Chapter:
Applications for Data Mining Techniques in Customer Relationship Management
Natalie Clewley (Brunel University, UK), Sherry Y. Chen (Brunel University, UK), and Xiaohui Liu (Brunel University, UK)
Copyright: © 2009
|Pages: 5
DOI: 10.4018/978-1-60566-026-4.ch033
Abstract
With the explosion in the amount of data produced in commercial environments, organizations are faced with the challenge of how to collect, analyze, and manage such large volumes of data. As a consequence, they have to rely upon new technologies to efficiently and automatically manage this process. Data mining is an example of one such technology, which can help to discover hidden knowledge from an organization’s databases with a view to making better business decisions (Changchien & Lu, 2001). Data mining, or knowledge discovery from databases (KDD), is the search for valuable information within large volumes of data (Hand, Mannila & Smyth, 2001), which can then be used to predict, model or identify interrelationships within the data (Urtubia, Perez-Correa, Soto & Pszczolkowski, 2007). By utilizing data mining techniques, organizations can gain the ability to predict future trends in both the markets and customer behaviors. By providing detailed analyses of current markets and customers, data mining gives organizations the opportunity to better meet the needs of its customers. With such significance in mind, this chapter aims to investigate how data mining techniques can be applied in customer relationship management (CRM). This chapter is organized as follows. Firstly, an overview of the main functionalities data mining technologies can provide is given. The following section presents application examples where data mining is commonly applied within the domain, with supporting evidence as to how each enhances CRM processes. Finally, current issues and future research trends are discussed before the main conclusions are presented.