PGLCM: efficient parallel mining of closed frequent gradual itemsets

Numerical data (e.g., DNA micro-array data, sensor data) pose a challenging problem to existing frequent pattern mining methods which hardly handle them. In this framework, gradual patterns have been recently proposed to extract covariations of attributes, such as: “When X increases, Y decreases”. T...

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Bibliographic Details
Published in:Knowledge and information systems 2015-06, Vol.43 (3), p.497-527
Main Authors: Do, Trong Dinh Thac, Termier, Alexandre, Laurent, Anne, Negrevergne, Benjamin, Omidvar-Tehrani, Behrooz, Amer-Yahia, Sihem
Format: Article
Language:English
Subjects:
Age
Algorithms
Analysis
Computer Science
Constants
Data mining
Data Mining and Knowledge Discovery
Database Management
Datasets
Deoxyribonucleic acid
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Microprocessors
Parallel processing
Pattern analysis
Pattern recognition
Processors
Regular Paper
State of the art
Studies
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Summary:Numerical data (e.g., DNA micro-array data, sensor data) pose a challenging problem to existing frequent pattern mining methods which hardly handle them. In this framework, gradual patterns have been recently proposed to extract covariations of attributes, such as: “When X increases, Y decreases”. There exist some algorithms for mining frequent gradual patterns, but they cannot scale to real-world databases. We present in this paper GLCM, the first algorithm for mining closed frequent gradual patterns, which proposes strong complexity guarantees: the mining time is linear with the number of closed frequent gradual itemsets. Our experimental study shows that GLCM is two orders of magnitude faster than the state of the art, with a constant low memory usage. We also present PGLCM, a parallelization of GLCM capable of exploiting multicore processors, with good scale-up properties on complex datasets. These algorithms are the first algorithms capable of mining large real world datasets to discover gradual patterns.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-014-0749-8