Abstract
Mining maximal frequent itemsets in data streams is more difficult than mining them in static databases for the huge, high-speed and continuous characteristics of data streams. In this paper, we propose a novel one-pass algorithm called FpMFI-DS, which mines all maximal frequent itemsets in Landmark windows or Sliding windows in data streams based on FP-Tree. A new structure of FP-Tree is designed for storing all transactions in Landmark windows or Sliding windows in data streams. To improve the efficiency of the algorithm, a new pruning technique, extension support equivalency pruning (ESEquivPS), is imported to it. The experiments show that our algorithm is efficient and scalable. It is suitable for mining MFIs both in static database and in data streams.
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Ao, F., Yan, Y., Huang, J., Huang, K. (2007). Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_36
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DOI: https://doi.org/10.1007/978-3-540-73499-4_36
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