数据挖掘与知识发现发展趋势-Trends in Data Mining and Knowledge Discovery

数据挖掘与知识发现发展趋势,值得参考!

1. Trends in Data Mining and Knowledge Discovery

Krzysztof J. Cios1,2,3 and Lukasz A. Kurgan4

1University of Colorado at Denver and Health Sciences Center, Department of Computer Science and Engineering, Campus Box 109, Denver, CO 80217-3364, U.S.A.; email: University of Colorado at Boulder, Department of Computer Science, Boulder, CO, U.S.A.;

4cData, LLC, Golden, CO 80401

University of Alberta, Department of Electrical and Computer Engineering, ECERF 2nd floor, Edmonton, AB T6G 2V4, Canada; 234

Data mining and knowledge discovery (DMKD) is a fast-growing field of research. Its popularity is caused by an ever increasing demand for tools that help in revealing and comprehending information hidden in huge amounts of data. Such data are generated on a daily basis by federal agencies, banks, insurance companies, retail stores, and on the WWW. This explosion came about through the increasing use of computers, scanners, digital cameras, bar codes, etc. We are in a situation where rich sources of data, stored in databases, warehouses, and other data repositories, are readily available but not easily analyzable. This causes pressure from the federal, business, and industry communities for improvements in the DMKD technology. What is needed is a clear and simple methodology for extracting the knowledge hidden in the data. In this chapter, an integrated DMKD process model based on technologies like XML, PMML, SOAP, UDDI, and OLE BD-DM is introduced. These technologies help to design flexible, semiautomated, and easy-to-use DMKD models to enable building knowledge repositories and allowing for communication between several data mining tools, databases, and knowledge repositories. They also enable integration and automation of the DMKD tasks. This chapter describes a six-step DMKD process model and its component technologies.

1.1 Knowledge Discovery and Data Mining Process Knowledge discovery (KD) is a nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns from large collections of data [30]. One of the crucial KD steps is a data mining (DM) step. DM is concerned with the actual extraction of knowledge from data, in contrast to the KD process, which is concerned with many other activities. We want to stress this distinction, although people often use the terms DM, KD and DMKD as synonymous.

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