Study of Association Rule Mining Algorithms at Single Level of Abstraction
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Abstract
Data mining helps in performing automated extraction and generating predictive information from large amount of data. The discovery
of interesting association relationships among itemsets in large database which consist of transactions has been described as an important
database mining problem and several algorithms for mining frequent pattern at single level have been developed. In this paper, we are reviewing
different algorithms such as Apriori, FP-growth, Partition based algorithm, Incremental update (FUp based, probability based), Boolean
Compress technique, Lattice Based Approach ,Fast algorithm to extract association rules from large itemsets.
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Keywords: Single-Level Association Rules, Data mining, support, Confidence
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