TY  - JOUR
T1  - Efficient Association Rules for Data Mining
AU - , C.M. Velu AU - , M. Ramakrishnan AU - , V. Somu AU - , P. Loganathan AU - , P. Vivekanandan 
JO  - International Journal of Soft Computing
VL  - 2
IS  - 1
SP  - 21
EP  - 36
PY  - 2007
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2007.21.36
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2007.21.36
KW  - Transaction database
KW  -frequent itemet
KW  -clusters
KW  -association rules
KW  -minimal support threshold
KW  -support monotonicity
KW  -correlation
AB  - Frequent Item Sets (FIS) play an essential role in many Data Mining (DM) tasks. We want to find interesting patterns from databases (DBs), such as Association Rules (ARs), correlations, classifiers, clusters and many more. The motivation for searching Ars to examine customer’s buying behavior. ARs describe how often items are dependent on each other to purchase together. For example, an AR beer 100%   chips 80% states that four of five customers that bought beer also bought chips. Such rules can be useful for decisions concerning product pricing, promotions, store layout and many others. Since their introduction in 1993 by Argawal <I>et al</I>., the FIS and AR mining problems have received a great deal of attention. During the past decade, hundreds of papers have been published to solve these mining problems more efficiently. In this study, we explain the basic FIS and compare various AR algorithms to extract required information from DBs. We describe the main techniques used to solve these problems.
ER  -