An efficient itemset mining approach for data streams
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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Mining frequent patterns from transaction database, time series and data stream is an important task now. Last decade, there are mainly two kinds of algorithms on frequent pattern mining. One is Apriori based on generating and testing, the other is FP-growth based on dividing and conquering, which has been widely used in static data mining. But with the new requirements of data mining, mining frequent pattern is not restricted in the static datasets any more. For data stream, the frequent pattern mining algorithms must have strong ability of updating and adjusting to further improve its efficiency. This paper proposes a novel structure NC-Tree (New Compact Tree), which can recode and filter original data to compress dataset. At the same time, a new frequent pattern mining algorithm is introduced base on it, which can update and adjust the tree more efficiently. The experiments show the structure and algorithm obviously improves mining efficiency and ensures high accuracy.