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.

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