Mining Articulate Association Rules from Closed Item Sets: A Counter Support Measurement Approach

Provided by: International Journal of Computer Applications
Topic: Big Data
Format: PDF
In this paper, it has been observed that a frequent item set mining algorithm are supposed to mine the closed ones as the finish results in a compact and a complete progress set and enhanced potency. However, the latest closed item set mining algorithms works with both candidate maintenance and check paradigm hand in hand, which proves to be friendlier in runtime, as in case of area usage when support threshold is a reduced entity or the item sets gets long. In this paper, the authors have shown, CEG&REP with CSM (Counter Support Measurement) that is supposed to be a more efficient approach which can be utilized for mining articulate association rules from closed sequences.

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