tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 106

Data Mining and Knowledge Discovery Handbook, 2 Edition part 106. 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. | 1030 Steve Moyle setting. Such well defined and strong processes include for instance clear model evaluation procedures Blockeel and Moyle 2002 . Different perspectives exist on what collaborative Data Mining is this is discussed further in section . Three interpretations are 1 multiple software agents applying Data Mining algorithms to solve the same problem 2 humans using modern collaboration techniques to apply Data Mining to a single defined problem 3 Data Mining the artifacts of human collaboration. This chapter will focus solely on the second item - that of humans using collaboration techniques to apply data mining to a single task. With sufficient definition of a particular Data Mining problem this is similar to a multiple software agent Data Mining framework the first item although this is not the aim of the chapter. Many of the difficulties encountered in human collaboration will also be encountered in designing a system for software agent collaboration. Collaborative Data Mining aims to combine the results generated by isolated experts by enabling the collaboration of geographically dispersed laboratories and companies. For each Data Mining problem a virtual team of experts is selected on the basis of adequacy and availability. Experts apply their methods to solving the problem - but also communicate with each other to share their growing understanding of the problem. It is here that collaboration is key. The process of analyzing data through models has many similarities to experimental research. Like the process of scientific discovery Data Mining can benefit from different techniques used by multiple researchers who collaborate compete and compare results to improve their combined understanding. The rest of this chapter is organized as follows. The potential difficulties in remote collaboration and a framework for analyzing such difficulties are outlined. A standard Data Mining process is reviewed and studied for the likely contributions that can .