Title of article :
Incremental updates of closed frequent itemsets over continuous data streams
Author/Authors :
Li، نويسنده , , Hua-Fu and Ho، نويسنده , , Chin-Chuan and Lee، نويسنده , , SUH-YIN LEE، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2009
Pages :
8
From page :
2451
To page :
2458
Abstract :
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.
Keywords :
data streams , DATA MINING , Single-pass mining , Incremental update , Closed frequent itemsets
Journal title :
Expert Systems with Applications
Serial Year :
2009
Journal title :
Expert Systems with Applications
Record number :
2345332
Link To Document :
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