A Perspective on Boolean Matrix for Mining Hybrid Dimensional Association Rules
Chaitanya B. Pednekar , R.C.Suryawanshi
The discovery of association rules in data mining is an important issue, the core of which is the frequent pattern mining, Apriori algorithm is traditional for the association rule mining, but it should repetitively scan the database and can produce number of candidates. We present an algorithm of mining hybrid dimension association rules which satisfies the definite condition on the basis of multidimensional transaction database. Boolean Matrix based approach has been employed to generate frequent item sets in multidimensional transaction databases. When using this algorithm first time, it scans the database once and will produce the association rules. Apriori property is used in algorithm to prune the item sets. It is not needed to scan the database again; it uses Boolean logical operations to generate the association rules. It is going to store data in the form of bits, so it needs less memory space.
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[Chaitanya B. Pednekar, R.C.Suryawanshi (2014), A Perspective on Boolean Matrix for Mining Hybrid Dimensional Association Rules, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-2, Issue-4, Page No-56-60], (ISSN 2347 - 5552). www.ijircst.org
Chaitanya B. Pednekar
Computer Engg, Mumbai University/ ACPCE,Kharghar, India. firstname.lastname@example.org