International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume5,March 2018,

Topic : Efficient Algorithms for Mining Erasable Closed Patterns from Product Datasets

Authors:Nikhil Gumaste || Sunita Nandgave

Abstract:Discovering information from expansive informational collections to use in intelligent systems turns out to be increasingly essential in the Internet period. Pattern mining, classification, text mining, and opinion mining are the topical issues. Among them, pattern mining is a important issue. The issue of mining erasable patterns (EPs) has been proposed as a variation of frequent pattern mining for optimizing the generation plan of production factories. A few algorithms have been proposed for effectively mining EPs. Be that as it may, for extensive limit esteems, many EPs are acquired, prompting substantial memory use. In this manner, it is important to mine a consolidated portrayal of EPs. This paper first defines erasable closed patterns (ECPs), which can represent to the set of EPs without data loss. At that point, a theorem for quick deciding ECPs in view of dPidset structure is proposed and demonstrated. Next, two efficient algorithms [erasable closed patterns mining (ECPat) and dNC_Set based algorithm for erasable closed patterns mining (dNC-ECPM)] for mining ECPs in view of this theorem are proposed.

Keywords: Data mining, pattern mining, erasable pattern, erasable closed pattern

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DOI: 01.1617/vol5/iss3/pid48357


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Editor-in-Chief

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Dr. Allon Guez
Professor, Drexel University,
USA


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IMPACT FACTOR: 4.890

ISSN(Online):2394-2320

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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ACCEPTANCE RATIO: 28.69%
ARTICLES PUBLISHED:0521
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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
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