TY  - JOUR
T1  - Mining Both Positive and Negative Association Rules without
Extra Database Scans
AU - Manoj Patil, Ujwala AU - Patil, J.B. 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 22
SP  - 5915
EP  - 5920
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.5915.5920
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.5915.5920
KW  - Data mining
KW  -association rule mining
KW  -positive association rules
KW  -negative association rules
KW  -proposed
KW  -effectiveness
AB  - Data mining is getting increasing acceptance in science and business areas that need to identify and
represent certain dependencies between attributes. This dependency between the attributes is represented in
the form of association rules. Association rule mining discovers interesting correlations between
attributes in a database. All the traditional association rule mining algorithms were developed to find positive
associations between attributes, i.e., A→B whereas negative association rule is an implication of the form
A→⇁B, ⇁A→B, ⇁A→⇁B where A and B are database attributes, ⇁A⇁B are negations of database attributes. Here,
we propose an apriori based algorithm to find the both positive and negative associations between attributes.
Experimental results show the effectiveness and efficiency of the proposed algorithm without additional
database scans.
ER  -