Abstract:
Frequent pattern mining such as association rules,
clustering, and classification is one of the most central
areas in the data mining research. One of the foremost
processes in association rule mining is the discovering of
the frequent pattern. To draw on all substantial frequent
patterns from the sizable amount of transaction data,
various algorithms have been proposed. The proposed
research aims to mine frequent patterns from the sizable
amount of transaction database by using linked list. In this
method, first scanning the database, the count of frequent 1-
itemsets is searched using the hash map and for next
itemsets, it is stored in the linked list, second scanning the
database. The frequent 2-itemsets is generated using hash
table and so on. So, the proposed research needs only two
scans and this proposed method requires shorter processing
time and smaller memory space.
