A Support-Ordered Trie for Fast Frequent Itemset Discovery
IEEE Transactions on Knowledge and Data Engineering
Incremental itemset mining based on matrix apriori algorithm
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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At present, there are no association rule mining algorithms that are suitable for use in electronic commerce because they do not consider that new products are introduced and old ones are retired frequently and they assume that support thresholds do not change. In this paper, a new algorithm called "Fast Online Dynamic Association Rule Mining" (FOLDARM) is introduced for mining in electronic commerce. It uses a novel tree structure known as a "Support-Ordered Trie Itemset" (SOTrieIT) structure to hold pre-processed transactional data. It allows FOLDARM to generate large 1-itemsets and 2-itemsets quickly without scanning the database. In addition, the SOTrieIT structure can be easily and quickly updated when transactions are added or removed. It also stores data that is independent of the support threshold and thus can be used for mining with varying support thresholds without any degradation in performance. Experiments have shown that FOLDARM outperforms Apriori, a classic mining algorithm, by up to two orders of magnitude (100 times).