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Author Flores, M.Q. ♦ Del Razo, F. ♦ Laurent, A. ♦ Poncelet, P. ♦ Sicard, N.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Correlation ♦ Sparse matrices ♦ Indexes ♦ Data mining ♦ Memory management ♦ Fuzzy sets ♦ sparse matrix formats ♦ Gradual pattern mining ♦ fuzzy orderings ♦ fuzzy gradual patterns
Abstract In this paper, we study the mining of gradual patterns in the presence of numeric attributes belonging to data sets. The field of gradual pattern mining have been recently proposed to extract covariations of attributes, such as: {the higher the age, the higher the salary}. This gradual pattern denoted as {size≥salary≥} means that the age of people increases together with their salary. Actually, the analysis of such correlations is very memory consuming. When managing huge databases, issue is very challenging. In this context, we focus on the use of fuzzy orderings to take this into account and we propose techniques in order to optimize the computation. These techniques are based on a matrix representation of fuzzy concordance degrees C(i; j) and the Yale Sparse Matrix Format.
Description Author affiliation: University Montpellier 2 - LIRMM France (Flores, M.Q.; Laurent, A.; Poncelet, P.) || Toluca Institute of Technology Mexico (Del Razo, F.) || EFREI - AllianSTIC France (Sicard, N.)
ISBN 9781467315074
ISSN 10987584
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-06-10
Publisher Place Australia
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781467315067
Size (in Bytes) 1.74 MB
Page Count 8
Starting Page 1
Ending Page 8

Source: IEEE Xplore Digital Library