A Survey of Efficient Algorithms and New Approach for Fast Discovery of Frequent Itemset for Association Rule Mining (DFIARM)
Anurag Choubey1, Ravindra Patel2, J.L. Rana3

1Anurag Choubey, Dean Academic, Technocrats Institute of Technology, Bhopal (Madhya Pradesh), India.
2Dr. Ravindra Patel, Reader & Head, Department of Computer Application, UIT-RGPV, Bhopal(M.P.), India Bhopal (Madhya Pradesh), India.
2Dr. J. L. Rana, Ex. Professor & Head, Department of Computer Science & Engineering, MANIT, Bhopal (Madhya Pradesh), India
Manuscript received on April 25, 2011. | Revised Manuscript received on May 01, 2011. | Manuscript published on May 05, 2011. | PP: 62-67 | Volume-1 Issue-2, May 2011. | Retrieval Number: A040051211
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The problem of mining association rules has attracted lots of attention in the research community. Several techniques for efficient discovery of association rule have appeared. With abundant literature published in research into frequent itemset mining and deriving association rules, if the question is raised that whether we have solved most of the critical problems related to frequent itemset mining and association rule discovery. Based on the scope of the recent literature, the answer will be negative. The most time consuming operation in discovering association rule, is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. Can one develop a method that may avoid or reduce candidate generation and test and utilize some novel data structures to reduce the cost in frequent pattern mining? This is the motivation of my study for mining frequent-item sets and association rules. In this paper we review some existing algorithms for frequent itemset mining and present a proposal of our new approach.
Keywords: Data mining, Frequent Item-set mining, Association Rule Mining.