Different Data Mining Algorithms: a Performance Analysis

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.