Abstract
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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Seyfi, M., Geva, S., Nayak, R. (2014). Mining Discriminative Itemsets in Data Streams. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_10
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DOI: https://doi.org/10.1007/978-3-319-11749-2_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11748-5
Online ISBN: 978-3-319-11749-2
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