Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Algorithms 2013, 6(4), 747-761; doi:10.3390/a6040747
Article

Multi-Core Parallel Gradual Pattern Mining Based on Multi-Precision Fuzzy Orderings

1
, 2
, 3
, 2,*  and 4
Received: 17 September 2013; in revised form: 16 October 2013 / Accepted: 21 October 2013 / Published: 1 November 2013
(This article belongs to the Special Issue Algorithms for Multi Core Parallel Computation)
Download PDF [1431 KB, uploaded 1 November 2013]
Abstract: Gradual patterns aim at describing co-variations of data such as the higher the size, the higher the weight. In recent years, such patterns have been studied more and more from the data mining point of view. The extraction of such patterns relies on efficient and smart orderings that can be built among data, for instance, when ordering the data with respect to the size, then the data are also ordered with respect to the weight. However, in many application domains, it is hardly possible to consider that data values are crisply ordered. When considering gene expression, it is not true from the biological point of view that Gene 1 is more expressed than Gene 2, if the levels of expression only differ from the tenth decimal. We thus consider fuzzy orderings and fuzzy gamma rank correlation. In this paper, we address two major problems related to this framework: (i) the high memory consumption and (ii) the precision, representation and efficient storage of the fuzzy concordance degrees versus the loss or gain of computing power. For this purpose, we consider multi-precision matrices represented using sparse matrices coupled with parallel algorithms. Experimental results show the interest of our proposal.
Keywords: parallel data mining; gradual patterns; fuzzy rank parallel data mining; gradual patterns; fuzzy rank
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Sicard, N.; Aryadinata, Y.S.; Del Razo Lopez, F.; Laurent, A.; Flores, P.M.Q. Multi-Core Parallel Gradual Pattern Mining Based on Multi-Precision Fuzzy Orderings. Algorithms 2013, 6, 747-761.

AMA Style

Sicard N, Aryadinata YS, Del Razo Lopez F, Laurent A, Flores PMQ. Multi-Core Parallel Gradual Pattern Mining Based on Multi-Precision Fuzzy Orderings. Algorithms. 2013; 6(4):747-761.

Chicago/Turabian Style

Sicard, Nicolas; Aryadinata, Yogi S.; Del Razo Lopez, Federico; Laurent, Anne; Flores, Perfecto M.Q. 2013. "Multi-Core Parallel Gradual Pattern Mining Based on Multi-Precision Fuzzy Orderings." Algorithms 6, no. 4: 747-761.

Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert