Decomposition methodology for classification tasks

• Published in: 2005 IEEE International Conference on Granular Computing Vol. 2; pp. 636 - 641 Vol. 2
• Main Authors: Rokach, L., Mainon, O.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Data analysis; Data mining; Economic forecasting; Engineering management; Industrial engineering; Machine learning; Neural networks; Operations research; Statistics
• ISBN: 0780390172; 9780780390171
• DOI: 10.1109/GRC.2005.1547369

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 paper 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.