TY - GEN
T1 - Enhanced mining association rule algorithm with reduced time & space complexity
AU - Mundra, Punit
AU - Maurya, Amit K.
AU - Singh, Sanjay
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In this paper, we have proposed a technique to improve the performance of existing mining association rule algorithm which significantly reduces the time and space complexity of independent of datasets. There are many data mining algorithms for finding association rules our contribution can be used in almost all of the algorithms independent of its variety. In this paper we are concentrating more on Apriori algorithm which is a type of candidate generation algorithm also a fundamental block of all the mining algorithms, rectifying its major limitation of consuming ample amount of time in generating the candidates.
AB - In this paper, we have proposed a technique to improve the performance of existing mining association rule algorithm which significantly reduces the time and space complexity of independent of datasets. There are many data mining algorithms for finding association rules our contribution can be used in almost all of the algorithms independent of its variety. In this paper we are concentrating more on Apriori algorithm which is a type of candidate generation algorithm also a fundamental block of all the mining algorithms, rectifying its major limitation of consuming ample amount of time in generating the candidates.
UR - https://www.scopus.com/pages/publications/84874155184
UR - https://www.scopus.com/pages/publications/84874155184#tab=citedBy
U2 - 10.1109/INDCON.2012.6420782
DO - 10.1109/INDCON.2012.6420782
M3 - Conference contribution
AN - SCOPUS:84874155184
SN - 9781467322720
T3 - 2012 Annual IEEE India Conference, INDICON 2012
SP - 1105
EP - 1110
BT - 2012 Annual IEEE India Conference, INDICON 2012
T2 - 2012 Annual IEEE India Conference, INDICON 2012
Y2 - 7 December 2012 through 9 December 2012
ER -