International Journal of Computer Engineering and Technology (IJCET)

Source ID:00000005
Volume 7, Issue 1,January- February 2016, Pages 18-25, Page Count - 8


P. Alagesh Kannan (1) E. Ramaraj (2)

(1) Assistant Professor, Department of Computer Science, Madurai Kamaraj University, Madurai, India.
(2) Professor, Department of Computer Science and Engineering, Alagappa University, Karaikudi, India.

Manuscript ID:- 00000-67990
Access Type : Open Access
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Cite this article:P. Alagesh Kannan,E. Ramaraj,  A Novel Approach To Mine Frequent Patterns From Large Volume Of Dataset Using Mdl Reduction Algorithm, International Journal of Computer Engineering and Technology(IJCET), 2016, 7(1), PP.18-25

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In this paper, MDL based reduction in frequent pattern is presented. The ideal outcome of any pattern mining process is to explore the data in new insights. And also, we need to eliminate the non-interesting patterns that describe noise. The major problem in frequent pattern mining is to identify the interesting patterns. Instead of performing association rule mining on all the frequent item sets, it is feasible to select a sub set of frequent item sets and perform the mining task. Selecting a small set of frequent item sets from large amount of interesting ones is a dif icult task. In our approach, MDL based algorithm is used for reducing the number of frequent item sets to be used for association rule mining is presented. MDL based approach provides good reduction of frequent patterns on all types of data such as sequences and trees. Experimental results show that reductions up to three orders of magnitude is achieved when MLD algorithm is used
Author Keywords
Frequent item sets Pattern Mining MDL Minimum Description Length Interestingness Data Mining Association Rule Mining and ARM

ISSN Print: 0976-6367 ISSN Online: 0976-6375
Source Type: Journals Document Type: Journal Article
Publication Language: English DOI:
Abbreviated Journal Title: IJCET Access Type: Open Access
Publisher Name: IAEME Publication Resource Licence: CC BY-NC
Major Subject:Physical Sciences Subject Area classification: Computer Science
Subject area: Computer Vision and Pattern Recognition Source: SCOPEDATABASE