Scopus
YÖKSİS ISSN Eşleşti
SJR Q1
MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules
Applied Soft Computing Journal · Ocak 2008
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Makale Bilgileri
ISSN15684946
Yayın TarihiOcak 2008
Cilt / Sayfa8 · 646-656
Scopus ID2-s2.0-34548472851
Özet
In this paper, a Pareto-based multi-objective differential evolution (DE) algorithm is proposed as a search strategy for mining accurate and comprehensible numeric association rules (ARs) which are optimal in the wider sense that no other rules are superior to them when all objectives are simultaneously considered. The proposed DE guided the search of ARs toward the global Pareto-optimal set while maintaining adequate population diversity to capture as many high-quality ARs as possible. ARs mining problem is formulated as a four-objective optimization problem. Support, confidence value and the comprehensibility of the rule are maximization objectives while the amplitude of the intervals which conforms the itemset and rule is minimization objective. It has been designed to simultaneously search for intervals of numeric attributes and the discovery of ARs which these intervals conform in only single run of DE. Contrary to the methods used as usual, ARs are directly mined without generating frequent itemsets. The proposed DE performs a database-independent approach which does not rely upon the minimum support and the minimum confidence thresholds which are hard to determine for each database. The efficiency of the proposed DE is validated upon synthetic and real databases. © 2007 Elsevier B.V. All rights reserved.
Yazarlar (3)
1
Bilal Alatas
2
Erhan Akin
ORCID: 0000-0001-6476-9255
3
Ali Karci
Anahtar Kelimeler
Data mining
Differential evolution
Evolutionary computation
Machine learning
Multi-objective optimization
Kurumlar
Firat Üniversitesi
Elazig Turkey
Son Atıflar
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Scimago Dergi (ISSN Eşleşmesi)
Applied Soft Computing
Q1
SJR Skoru1,511
H-Index208
YayıncıElsevier B.V.
ÜlkeNetherlands
Software (Q1)
Metrikler
196
Atıf
3
Yazar
5
Anahtar Kelime