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Title: Data mining and knowledge discovery
Authors: Wang, SL
Shi, WZ 
Issue Date: 2012
Publisher: Springer
Source: In W Kresse & MD Dvid (Eds.), Springer handbook of geographic information, p. 123-144. Berlin ; New York: Springer, 2012 How to cite?
Abstract: In this chapter, data mining and knowledge discovery (DMKD) is presented with basic concepts, a brief history of its evolution, mathematical foundations, and usable techniques, along with the data warehouse and the decision support system (DSS). First, dataset and knowledge will be defined and elucidated as under DMKD. DMKD is a discovery process with different hierarchies, granularities, and/or scales. For a set of concepts that may be best understood if being viewed and explained from various perspectives, the chapter starts with a definition followed by a table explaining DMKD from different views (Sect. 5.1). The evolution of DMKD is then briefly tracked from the rapid advance in massive data to the birth of DMKD (Sect. 5.2). Some mathematical foundations are given in Sect. 5.3, i.e. probability theory, statistics, fuzzy set, rough set, data fields, and cloud models. Section 5.4 introduces some usable DMKD techniques. DMKD is used to discover a set of rules and exceptions with association, classification, clustering, prediction, discrimination, and exception detection. In Sects. 5.5 and 5.6, data warehouses and decision support systems are given. The first one mentioned is one of the data sources for DMKD, and DMKD is a new technique to assist the latter with a task. Finally, trends and perspectives are summarized and forecasted into two promising fields, web mining and spatial data mining.
ISBN: 9783540726807 (electronic bk.)
3540726802 (electronic bk.)
9783540726784 (print)
DOI: 10.1007/978-3-540-72680-7_5
Appears in Collections:Book Chapter

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