  As with the development of the IT technologies, the amount of accumulated
data is also increasing. Thus the role of data mining comes into picture.
Association rule mining becomes one of the significant responsibilities of
descriptive technique which can be defined as discovering meaningful patterns
from large collection of data. The frequent pattern mining algorithms determine
the frequent patterns from a database. Mining frequent itemset is very
fundamental part of association rule mining. Many algorithms have been proposed
from last many decades including majors are Apriori, Direct Hashing and
Pruning, FP-Growth, ECLAT etc. The aim of this study is to analyze the existing
techniques for mining frequent patterns and evaluate the performance of them by
comparing Apriori and DHP algorithms in terms of candidate generation, database
and transaction pruning. This creates a foundation to develop newer algorithm
for frequent pattern mining.
