J-PMCRI: a methodology for inducing pre-pruned modular classification rules

F. Stahl, Max Bramer, Mo Adda

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Abstract

Inducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top-Down Induction of Decision Trees (TDIDT), also known as the ‘divide and conquer’ approach, however powerful alternative algorithms exist that induce modular rules. Most of these alternative algorithms follow the ‘separate and conquer’ approach of inducing rules, but very little work has been done on making the ‘separate and conquer’ approach scale better on large training data. This seminar examines the potential of the recently developed blackboard based J-PMCRI methodology for parallelising modular classification rule induction algorithms that follow the ‘separate and conquer’ approach. A concrete implementation of the methodology is evaluated empirically on very large datasets.
Original languageEnglish
Pages (from-to)47-56
Number of pages10
JournalIFIP Advances in Information and Communication Technology
Volume331
DOIs
Publication statusPublished - 2010

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  • Optimisation of extended generalised fat tree topologies

    Peratikou, A. & Adda, M., 2014, Distributed computer and communication networks: 17th international conference, DCCN 2013, Moscow, Russia, October 7-10, 2013. revised selected papers. Vishnevsky, V., Kozyrev, D. & Larionov, A. (eds.). Heidelberg: Springer, p. 82-90 (Communications in computer and information science ; vol. 279).

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