Partitioning large data to scale up lattice-based algorithm

Huaiguo Fu, Engelbert Mephu Nguifo

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)

Abstract

Concept lattice is an effective tool and platform for data analysis and knowledge discovery such as classification or association rules mining. The lattice algorithm to build formal concepts and concept lattice plays an essential role in the application of concept lattice. We propose a new efficient scalable lattice-based algorithm: ScalingNextClosure to decompose the search space of any huge data in some partitions, and then generate independently concepts (or closed itemsets) in each partition. The experimental results show the efficiency of this algorithm.

Original languageEnglish
Pages (from-to)537-541
Number of pages5
JournalProceedings of the International Conference on Tools with Artificial Intelligence
Publication statusPublished - 2003
Externally publishedYes
EventProceedings: 15th IEEE International Conference on Tools with artificial Intelligence - Sacramento, CA, United States
Duration: 03 Nov 200305 Nov 2003

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