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Global Journal of Computer Science and Technology
Software & Data Engineering
Volume 12 Issue 15 Version 1.0 Year 2012
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals Inc. (USA)
Online ISSN: 0975-4172 & Print ISSN: 0975-4350
Encapsulation of Soft Computing Approaches within Itemset
Mining – A Survey
By Jyothi Pillai & O.P.Vyas
Bhilai Institute of Technology, Durg, Chhattisgarh, India
Abstract - Data Mining discovers patterns and trends by extracting knowledge from large databases.
Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc.
aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and
low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of
uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide
efficient search algorithms for selecting a model, from mixed media data. Data mining refers to
information extraction while soft computing is used for information processing. For effective
knowledge discovery from large databases, both Soft Computing and Data Mining can be merged.
Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and
corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in
discovered patterns. This survey paper explores the usage of soft computing approaches in itemset
utility mining.
Keywords : data mining, soft computing, itemset mining, fuzzy logic, neural networks, genetic
algorithm.
GJCST-C Classification : H.2.8
EncapsulationofSoftComputingApproacheswithinItemsetMiningASurvey
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