Abstract
Frequent pattern mining plays an important role in the data mining community since it is usually a fundamental step in various mining tasks. However, maintenance of frequent patterns is very expensive in the incremental database. In addition, the status of a pattern changes with time. In other words, a frequent pattern is possible to become infrequent, and vice versa. In order to exactly find all frequent patterns, most algorithms have to scan the original database completely whenever an update occurs. In this paper, we propose a new algorithm iTM, stands for incremental Transaction Mapping algorithm for incremental frequent pattern mining without rescanning the whole database. It transfers the transaction dataset to the vertical representation such that the incremental dataset can be integrated to the original database easily. As demonstrated in our experiments, the proposed method is very efficient and suitable for mining frequent patterns in the incremental database.
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Dai, BR., Lin, PY. (2009). iTM: An Efficient Algorithm for Frequent Pattern Mining in the Incremental Database without Rescanning. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_77
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DOI: https://doi.org/10.1007/978-3-642-02568-6_77
Publisher Name: Springer, Berlin, Heidelberg
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