Abstract
Online mining the maximal frequent itemsets over data streams is an important problem in data mining. In order to solve mining maximal frequent itemsets from data streams using the Landmark Window model, Mao et al. propose the INSTANT algorithm. The structure of the INSTANT algorithm is simple and it can save much memory space. But it takes long time in mining the maximal frequent itemsets. When the new transaction comes, the number of comparisons between the old transactions of the INSTANT algorithm is too much. Therefore, in this chapter, we propose the Set-Checking algorithm to mine frequent itemsets from data streams using the Landmark Window model. We use the structure of the lattice to store our information. The structure of the lattice records the subset relationship between the child node and the parent node. From our simulation results, we show that the process time of our Set-Checking algorithm is faster than that of the INSTANT algorithm.
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Acknowledgments
The research was supported in part by the National Science Council of Republic of China under Grant No. NSC-101-2221-E-110-091-MY2.
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Chang, YI., Tsai, MH., Li, CE., Lin, PY. (2013). A Set-Checking Algorithm for Mining Maximal Frequent Itemsets from Data Streams. In: Juang, J., Huang, YC. (eds) Intelligent Technologies and Engineering Systems. Lecture Notes in Electrical Engineering, vol 234. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6747-2_29
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DOI: https://doi.org/10.1007/978-1-4614-6747-2_29
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