Abstract:
Association rule mining is a technique to find
useful patterns and associations in transactional
databases. The mining of association rules can be
mapped into the problem of discovering large
(frequent) itemsets where is a grouped of items which
appear in a sufficient number of transaction. The
discovery of interesting association relationships
among huge amount of business transaction records
can help in many business decision making process .
There are many association rules mining algorithms.
But this system is intended to make the comparative
study of three association rules mining algorithms
such as DHP algorithm, PHP algorithm and Hybrid
Approach of Support-Ordered Tree and PHP based
on same dataset. Both DHP and PHP algorithm use
hash base method and pruning method to reduce
database size. DHP use direct hashing technique.
PHP use perfect hashing technique. The two dataset,
Kyar Nyo Pan Stationary Store and Orange
minimarket are used.
