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Constraint-Based, Multidimensional Data Mining
August 1999 (vol. 32 no. 8)
pp. 46-50

Although many data-mining methodologies and systems have been developed in recent years, the authors contend that by and large, present mining models lack human involvement, particularly in the form of guidance and user control. They believe that data mining is most effective when the computer does what it does best--like searching large databases or counting--and users do what they do best, like specifying the current mining session's focus. This division of labor is best achieved through constraint-based mining, in which the user provides restraints that guide a search. Mining can also be improved by employing a multidimensional, hierarchical view of the data. Current data warehouse systems have provided a fertile ground for systematic development of this multidimensional mining. Together, constraint-based and multidimensional techniques can provide a more ad hoc, query-driven process that effectively exploits the semantics of data than those supported by current stand-alone data-mining systems.

Citation:
Jiawei Han, Laks V.S. Lakshmanan, Raymond T. Ng, "Constraint-Based, Multidimensional Data Mining," Computer, vol. 32, no. 8, pp. 46-50, Aug. 1999, doi:10.1109/2.781634
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