A Study on Data Mining : Frequent Itemset Mining Methods Apriori, FP growth, Eclat

Authors(2) :-Dhinakaran D, Dr. Joe Prathap P M

Data mining is described as a process of discovering useful and interesting patterns hidden in huge amounts of data stored in multiple data sources. Data mining is a interdisciplinary field, ranging from Statistics, Database technology, Information recovery, Artificial intelligence, Machine learning, Pattern recognition, Neural networks, Knowledge-based systems, High-performance computing, and Data visualization have had impacts on the growth of data mining. Association rule mining is the core process in the field of data mining. It discover set of frequent items & generates ruleset within huge transaction databases. Data mining and its techniques can be enormously helpful in many fields such as business, education, government, fraud detection, and financial banking, future healthcare and so on. Data mining have a lot of merits but still data mining systems face lot of troubles and hazards. The purpose of this paper is to discuss the basic concepts of data mining, its various techniques , specifically about Frequent Itemset Mining Methods, various challenges, applications and important issues related to data mining.

Authors and Affiliations

Dhinakaran D
Assistant Professor, Department of Computer Science & Engineering, Peri Institute of Technology, Mannivakkam, Kanchipuram, India
Dr. Joe Prathap P M
Associate Professor, Department of Computer Science & Engineering, Rmd Engineering College, Kavaraipettai, Tiruvallur, India

Data Mining, Association Rule Mining, Frequent Itemset Mining, Transaction databases.

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 526-533
Manuscript Number : CSEIT1726165
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dhinakaran D, Dr. Joe Prathap P M, "A Study on Data Mining : Frequent Itemset Mining Methods Apriori, FP growth, Eclat ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.526-533, November-December-2017.
Journal URL : http://ijsrcseit.com/CSEIT1726165

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