Abstract:
Association rule mining, is one of the most
important and well researched techniques of data
mining. It is the process of discovering large itemsets
appeared in a sufficient number of transactions.
Large itemsets from a huge number of candidate
large itemsets are dominating factor for the overall
data mining performance. This paper presents
mining association rules among items in a large
database of sales transactions. To address this
problem, it present an effective hash-based algorithm
for the candidate set generation. This system applies
an algorithm DHP (Direct Hashing and Pruning) on
application cosmetic sales data to generate frequent
association patterns. Generation of smaller
candidate sets enables to effectively trim the
transaction database size at a much earlier stage of
the iterations, thereby reducing the computational
cost of later iterations significantly. The
experimental results of our system are also discussed
in this paper.
