tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 101

Data Mining and Knowledge Discovery Handbook, 2 Edition part 101. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 51 Data Mining using Decomposition Methods Lior Rokach1 and Oded Maimon2 1 Department of Information System Engineering Ben-Gurion University Beer-Sheba Israel liorrk@ 2 Department of Industrial Engineering Tel-Aviv University Ramat-Aviv 69978 Israel maimon@ Summary. The idea of decomposition methodology is to break down a complex Data Mining task into several smaller less complex and more manageable sub-tasks that are solvable by using existing tools then joining their solutions together in order to solve the original problem. In this chapter we provide an overview of decomposition methods in classification tasks with emphasis on elementary decomposition methods. We present the main properties that characterize various decomposition frameworks and the advantages of using these framework. Finally we discuss the uniqueness of decomposition methodology as opposed to other closely related fields such as ensemble methods and distributed data mining. Key words Decomposition Mixture-of-Experts Elementary Decomposition Methodology Function Decomposition Distributed Data Mining Parallel Data Mining Introduction One of the explicit challenges in Data Mining is to develop methods that will be feasible for complicated real-world problems. In many disciplines when a problem becomes more complex there is a natural tendency to try to break it down into smaller distinct but connected pieces. The concept of breaking down a system into smaller pieces is generally referred to as decomposition. The purpose of decomposition methodology is to break down a complex problem into smaller less complex and more manageable sub-problems that are solvable by using existing tools then joining them together to solve the initial problem. Decomposition methodology can be considered as an effective strategy for changing the representation of a classification problem. Indeed Kusiak 2000 considers decomposition as the most useful form of transformation of data sets . The .