Abstract:
Data mining (also called knowledge discovery in
databases) represents the process of extracting interesting
and previously unknown knowledge (patterns) from data. By
applying artificial intelligence together with analytical
methods data can be extracted. An association rule expresses
the dependence of a set of attribute-value pairs, called items,
upon another set of items (item set). . The association rule
mining algorithms can be classified into two main groups:
the level-wise algorithms and the tree-based algorithms. The
level-wise algorithms scan the entire database multiple time
but they have moderate memory requirement. The two phase
algorithms scan the database only twice but they can have
extremely large memory requirement. In this paper a
comparative study of the algorithms used in association rules
mining apriori and FP growth is done . A performance study
has been done which shows the advantages and disadvantage
of algorithms.