Abstract
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be straightforwardly applied to data streams which are continuous, unbounded, usually coming at high speed and often with a data distribution which changes with time. The main challenges of frequent pattern mining in data streams are: avoiding multiple scans of the entire dataset, optimizing memory usage and capturing distribution drift. To face these challenges, we propose a novel algorithm, which is based on a sliding window model in order to deal with efficiency issues and to keep up with distribution change. Each window consists of several slides. The generation of itemsets is local to each slide, while the estimation of their approximate support is based on the window. Efficiency in the generation of the itemsets is ensured by the usage of a synopsis structure, called SE-tree. Experiments prove the effectiveness of the proposed algorithm.
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Ciampi, A., Fumarola, F., Appice, A., Malerba, D. (2009). Approximate Frequent Itemset Discovery from Data Stream. In: Serra, R., Cucchiara, R. (eds) AI*IA 2009: Emergent Perspectives in Artificial Intelligence. AI*IA 2009. Lecture Notes in Computer Science(), vol 5883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10291-2_16
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DOI: https://doi.org/10.1007/978-3-642-10291-2_16
Publisher Name: Springer, Berlin, Heidelberg
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