A New Association Rule Mining Algorithm

Authors:
B. Chandra; Gaurav
Affiliations:
Indian Institute of Technology, Delhi, New Delhi, India 110 016;Indian Institute of Technology, Delhi, New Delhi, India 110 016
Venue:
Neural Information Processing
Year:
2008

Citing 5
Cited 0

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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases

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Abstract

A new algorithm called STAG (Stacked Graph) for association rule mining has been proposed in this paper using graph theoretic approach. A structure is built by scanning the database only once or at most twice that can be queried for varying levels of minimum support to find frequent item sets. Incremental growth is possible as and when new transactions are added to the database making it suitable for mining data streams. Transaction scanning is independent of the order of items in a transaction. Performance of this algorithm has been compared with other existing algorithms using popular datasets like the mushroom dataset, chess and connect dataset of the UCI data repository. The algorithm excels in performance when the dataset is dense.