Mining discriminative Itemsets in data streams
Seyfi, Majid, Geva, Shlomo, & Nayak, Richi (2014) Mining discriminative Itemsets in data streams. Lecture Notes in Computer Science : Web Information Systems Engineering – WISE 2014, 8786, pp. 125-134.
This paper presents a single pass algorithm for mining discriminative Itemsets in data streams using a novel data structure and the tilted-time window model. Discriminative Itemsets are defined as Itemsets that are frequent in one data stream and their frequency in that stream is much higher than the rest of the streams in the dataset. In order to deal with the data structure size, we propose a pruning process that results in the compact tree structure containing discriminative Itemsets. Empirical analysis shows the sound time and space complexity of the proposed method.
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|Item Type:||Journal Article|
|Additional Information:||15th International Conference, Thessaloniki, Greece, October 12-14, 2014, Proceedings, Part I|
|Keywords:||Data stream mining, Discriminative Itemsets, Tilted-time window model, Prefix tree|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 Springer International Publishing Switzerland|
|Deposited On:||19 Nov 2014 01:45|
|Last Modified:||20 Nov 2014 00:07|
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