Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams.
|Title of host publication||ICDM 2006: SIXTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, WORKSHOPS|
|State||Published - 2006|
|Event||6th IEEE International Conference on Data Mining - Hong Kong, China|
Duration: 18 Dec 2006 → 22 Dec 2006
|Conference||6th IEEE International Conference on Data Mining|
|Period||18/12/06 → 22/12/06|