Frequent Itemset mining over transactional data streams using Item-Order-Tree
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 8
Abstract
The association rule mining is one of the important area for research in data mining. In association rule mining online association rule mining is one of the hottest area due to the reason that the knowledge embedded in the data stream is more likely to be changed as time goes by. This paper proposes an algorithm as well as a data structure for online data mining. In this method the pruning in the data structure as well as the frequent itemset generation will be based on the request. The data structure which we introducing will have the capability to maintain the transactions in the sorted order. Every transaction can be extracted from the item-Order-Tree as by doing the traversal in depth. Frequent itemset can be generated as by do the traversal from the parent node that the user requested for. This ItemOrder-Tree improves the erformance of the online association rule mining.
Authors and Affiliations
Pramod S. , O. P. Vyas
Keywords
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