Abstract:
Data Mining is a fast developing field of
computer science and technology, which are
helpful to enable end users for decision making
process. One of the most important data mining
processes is that of Association Rule Mining. This
paper intends to the analysis on efficiency of the
two algorithms (Apriori and MBAT) which finding
frequent itemsets in Association Rule Mining. The
Association Rule Mining is based mainly on
discovering frequent itemsets. Apriori algorithm
and other popular Association Rule Mining
algorithms mainly generate a large number of
candidate items and scanning the database too
many times. To remove these deficiencies, this
paper presents a method named Matrix Based
Frequent Itemsets Minining algorithm with Tags
(MBAT) which can reduce the number of
candidate itemsets. In this paper, the system used
Java Programming Language with Follow Me
products dataset to compare these two algorithms.
