A Novel Strategy for Mining Frequent Closed Itemsets in Data Streams
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| Title | A Novel Strategy for Mining Frequent Closed Itemsets in Data Streams |
| Authors | |
| Abstract | Mining frequent itemsets from data stream is an important task in stream data mining. This paper presents an algorithm Stream_FCI for mining the frequent closed itemsets from data streams in the model of sliding window. The algorithm detects the frequent closed itemsets in each sliding window using a DFP-tree with a head table. In processing each new transaction, the algorithm changes the head table and modifies the DFP-tree according to the changed items in the head table. The algorithm also adopts a table to store the frequent closed itemsets so as to avoid the time-consuming operations of searching in the whole DFP-tree for adding or deleting transactions. Our experimental results show that our algorithm is more efficient and has lower time and memory complexity than the similar algorithms Moment and FPCFI-DS. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2012-07-01 |
| Source | Journal of Computers Vol 7, No 7 (2012): Special Issue: Current Research in Computer Science and Information Technology |
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