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.
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.