Sciweavers

Share
JIIS
2007

Towards a new approach for mining frequent itemsets on data stream

9 years 10 months ago
Towards a new approach for mining frequent itemsets on data stream
Mining frequent patterns on streaming data is a new challenging problem for the data mining community since data arrives sequentially in the form of continuous rapid streams. In this paper we propose a new approach for mining itemsets. Our approach has the following advantages: an efficient representation of items and a novel data structure to maintain frequent patterns coupled with a fast pruning strategy. At any time, users can issue requests for frequent itemsets over an arbitrary time interval. Furthermore our approach produces an approximate answer with an assurance that it will not bypass user-defined frequency and temporal thresholds. Finally the proposed method is analyzed by a series of experiments on different datasets.
Chedy Raïssi, Pascal Poncelet, Maguelonne Tei
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where JIIS
Authors Chedy Raïssi, Pascal Poncelet, Maguelonne Teisseire
Comments (0)
books