Abstract
Mining frequent itemsets from large databases has played an essential role in many data mining tasks. It is also important to maintain the discovered frequent itemsets for these data mining tasks when the database is updated. All algorithms proposed so far for the maintenance of discovered frequent itemsets are only performed with a fixed minimum support, which is the same as that used to obtain the discovered frequent itemsets. That is, users cannot change the minimum support even if the new results are unsatisfactory to the users. In this paper two new complementary algorithms, FMP (First Maintaining Process) and RMP (Repeated Maintaining Process), are proposed to maintain discovered frequent itemsets in the case that new transaction data are added to a transaction database. Both algorithms allow users to change the minimum support for the maintenance processes. FMP is used for the first maintaining process, and when the result derived from the FMP is unsatisfactory, RMP will be performed repeatedly until satisfactory results are obtained. The proposed algorithms re-use the previous results to cut down the cost of maintenance. Extensive experiments have been conducted to assess the performance of the algorithms. The experimental results show that the proposed algorithms are very resultful compared with the previous mining and maintenance algorithms for maintenance of discovered frequent itemsets.
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This work was supported in part by the National Grand Fundamental Research 973 Program of China under Grant No.G1999032705, and the Returned Overseas Scholar Research Foundation of the Ministry of Education of China.
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Du, X., Tang, S. & Akifumi, M. Maintaining discovered frequent itemsets: Cases for changeable database and support. J. Comput. Sci. & Technol. 18, 648–658 (2003). https://doi.org/10.1007/BF02947125
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DOI: https://doi.org/10.1007/BF02947125