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Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

Stahl, F. ORCID: and Bramer, M. (2012) Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks. Knowledge-Based Systems, 35. pp. 49-63. ISSN 0950-7051

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To link to this item DOI: 10.1016/j.knosys.2012.04.014


In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:30163
Uncontrolled Keywords:Parallel computing; Parallel rule induction; Modular classification rule induction; PMCRI; J-PMCRI; Prism


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