A Comparative Analysis Of Various Association Rule Mining Algorithms
Association rule, Data mining, Classification, Java Program, Association Rule Mining, Large Dataset, Apriori, Eclate, F-P Growth.
Association Rule Mining (ARM) is one of the major data mining methods used to mine hidden knowledge from databases that can be used by an organization's decision makers to increase overall profit. However, performing ARM needs frequent passes over the entire database. Clearly, for large database, the role of input/output overhead in scanning the database is very important. In this paper, we provide execution time related to association rule mining algorithms and survey the record of existing association rule mining methods and find out the fastest association rule mining algorithm. Obviously, a single article cannot be a entire review of the entire algorithms, yet we wish that the references cited will cover up the major theoretical issues, guiding the researcher in motivating research information that have yet to be explored.
"A Comparative Analysis Of Various Association Rule Mining Algorithms", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.2, Issue 5, page no.325 - 331, May-2017, Available :https://ijsdr.org/papers/IJSDR1705056.pdf
Volume 2
Issue 5,
May-2017
Pages : 325 - 331
Paper Reg. ID: IJSDR_170428
Published Paper Id: IJSDR1705056
Downloads: 000347043
Research Area: Engineering
Country: Abhanpur(Raipur), Chhattisgarh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
