IJMLC 2013 Vol.3(4): 347-351 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.335

Mining Association Rules Based on Boolean Algorithm - a Study in Large Databases

Chinta Someswara Rao, D. Ravi Babu, R. Shiva Shankar, V. Pradeep Kumar, J. Rajanikanth, and Ch. Chandra Sekhar

Abstract—With the wide applications of computers and automated data gathering tools, massive amounts of data have constantly collected in databases, which create immense demand for analyzing data and turning them into useful knowledge. Therefore, Knowledge Discovery and Data mining has become a research field in recent years to analyze the data in large databases. Association rule mining is one of the dominant methods for market basket analysis, which analyzes customer buying habits. The problem of association rule mining is that there are so many promising rules; it is obvious that such a vast amount of rules cannot be processed by inspecting each one. Therefore efficient algorithms restrict the search space and check only a subset of all rules. Boolean algorithm is one technique for mining association rules. The first objective of this study is to generate Association rules from massive databases in order to entrepreneurs can enlarge their own marketing strategies. The second objective of this study is to implement the program in the most efficient way in order to decrease the processing time and the memory consumption. This study can help retailers to build marketing strategies by gaining information about which items are frequently purchased together by customers.

Index Terms—KDD, databases, boolean algorithm.

Chinta Someswara Rao, D. Ravi Babu, R. Shiva Shankar, V. Pradeep Kumar, J. Rajanikanth are with the SRKR Engineering College, Bhimavaram, AP, India (e-mail: chinta.someswararao@ gmail.com). Ch. Chandra Sekhar was with AITAM, Tekkali, AP, India.

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Cite:Chinta Someswara Rao, D. Ravi Babu, R. Shiva Shankar, V. Pradeep Kumar, J. Rajanikanth, and Ch. Chandra Sekhar, "Mining Association Rules Based on Boolean Algorithm - a Study in Large Databases," International Journal of Machine Learning and Computing vol.3, no. 4, pp. 347-351, 2013.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net