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Efficient Mining of Maximal Frequent Concepts

Geetha, M and D'souza, R J (2010) Efficient Mining of Maximal Frequent Concepts. International Journal of Information Technology and Knowledge Management, 2 (2). pp. 623-626. ISSN 0973-4414

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Abstract

A frequent itemset is maximal if none of its supersets is frequent. In this paper we propose a method for mining Maximal Frequent Concepts using Quantitative Extended Concept Lattice for discovering maximal frequent concepts from databases which requires only one scan of the database. In mining association rules, the most time consuming job is to discover all frequent itemsets from a large database with respect to a given minimum support. Many sequential and parallel algorithms have been proposed to solve this problem. The most common sequential algorithms are Apriori and its variations. Apriori like algorithms employ a strict bottom-up, breadth-first search and enumerate every frequent itemsets. They require multiple passes over the database. The algorithm Basis Tree finds frequent concepts in a single scan of the database. But proposed algorithm is time efficient when compared to Basis tree algorithm..

Item Type: Article
Uncontrolled Keywords: Concept, Concept Indicator, Distributed, Global Mining, QECL, Support.
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 22 May 2014 07:32
Last Modified: 22 May 2014 07:32
URI: http://eprints.manipal.edu/id/eprint/139561

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