An Empirical Analysis of Multiple Level Association Rules Mining Method for Feature Extraction
Authors: Ruchika Yadav, Kanwal Garg
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Mining multiple level association rules in large databases is core area of data mining. Discovering these rules is favorable to the accurate and appropriate decision made by decision makers. Frequent patterns discovery is the key process in multiple levels association rule mining. One of the challenges in developing multiple level association rules mining
approaches is to implement an iterative procedure to find association rule, which takes a intricate transaction process.
Moreover, the offered mining methods cannot perform proficiently due to repetitive disk access overhead. Due to this, a novel method named MLTransTrie is presented in this paper. It can efficiently discover the association rules at multiple levels of abstraction in large databases and provide the more exact and precise information. The focus is on the comparative exploration and performance assessment of the MLTransTrie algorithm that generates multiple trie structure for all levels in one database scan. For this, the performance of this new algorithm MLTransTrie is analyzed and compared with LWFT and MLT2_L1 algorithm on diverse class of datasets (four real world dataset) and parameters.
Keywords- Concept Hierarchy; Confidence; Frequent Patterns; Minimum Support; MLTransTrie Implementation; Multiple-Level Association Rule.