Exploiting parallelism in knowledge discovery systems to improve scalability

Galal, Gehad; Cook, Diane J.; Holder, Lawrence B.

doi:10.1109/hicss.1998.648320

Cited by 1 publication

(2 citation statements)

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“…To achieve this goal, all instances of the best substructure at a given level are compressed to a single vertex and SUBDUE is invoked on the "reduced graph". Hierarchy of substructures obtained in this way is then used for various levels of interpretation; depending on goals of data analysis (see [1,6,8] for a complete description of SUBDUE and examples of its application to real-life problems).…”

Section: Subduementioning

confidence: 99%

“…The need to extract valuable information from this data challenges researchers to develop efficient techniques to discover and interpret interesting patterns in it. In this paper we are interested in discovering concepts in structural data, for which a number of algorithms have been proposed [1,6,8]. One of them, SUBDUE, discovers substructures on the basis of the minimum description length principle [8].…”

Section: Introductionmentioning

confidence: 99%

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Improving Parallelism in Structural Data Mining

Cai

Jónyer

Paprzycki

2006

Parallel Processing and Applied Mathematics

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Abstract. Large amount of data collected daily requires efficient algorithms for its processing. The SUBDUE data mining system discovers substructures in structurally complex data, based on the minimum description length principle. Its parallel implementation, MPI-SUBDUE, was created in 2001 to reduce computation time and/or to deal with larger datasets. In this paper, a new, more efficient implementation of MPI-SUBDUE is introduced. The experimental results show that, for the mutagenesis dataset, the new implementation outperforms the original one by up to 33% and that the performance gain increases with the number of processors used.

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Section: Subduementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%