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

Monthly Journal for Science , Computer Science and Engineering

Volume5,January 2018,

Topic : Energetic Data Mining Law To Identify Frequenty Item Sets over Huge Database via IFCM

Authors:Pragathi Vulpala ||

Abstract:Association Rule Mining is one of the most important tasks in data mining industry, especially in terms of huge database handling strategies; this association rule mining plays a vital role to deal with the commercial and non-commercial data. The main process of association rule mining is to identify the frequent items from the itemset. In past analysis there are several methodologies available to identify the frequent items over the inputting itemset. Most of the researchers and developers commonly used Apriori' and Frequent Pattern-Tree algorithms to identify the frequent items over the itemset. Generally Apriori investigates multiple number of iterations over the huge databases to identify the frequent items over the inputting itemset, globally Apriori law follows the candidate introduction process for identifying the frequent items. The approach of Frequent Pattern-Tree algorithm is different from Apriori law, it investigates the database twice without' includes the generation of candidates. In this proposed system, a new algorithm is proposed to improve the time constraint and accuracy levels of identifying the frequent items over the large itemset database, called Intelligent Frequency Count Model [IFCM]. This algorithm of IFCM uses the scanning method different from the past two terminologies such as multiple scanning and twice scanning schemes, instead of these two concepts, the IFCM performs scanning with one time as well as it belongs with the candidate generation process. So, this methodology is also termed as Hybrid Intelligent Frequency Count Model [HIFCM]. For all the proposed logic, clearly shows that the IFCM provides better results compare to Apriori and Frequent Pattern-Tree process..

Keywords: Frequency Count, Itemset, Intelligent Frequency Count Model, IFCM, Hybrid IFCM, HIFCM

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DOI: 01.1617/vol5/iss1/pid10382

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Dr. Allon Guez
Professor, Drexel University,

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

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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
Frequency : monthly
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