Interactive mining of top-K frequent closed itemsets from data streams |
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| Authors: | Hua-Fu Li |
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| Affiliation: | 1. Department of Computer Science and Engineering, University of Dhaka, Bangladesh;2. ICube Laboratory, University of Strasbourg, France;3. Department of Computer Science, University of Manitoba, Canada;1. Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, DE-53754 Sankt Augustin, Germany;2. Information Systems and Database Technology, RWTH Aachen University, DE-52056 Aachen, Germany;3. Department of Computer Science & Engineering, University of Dhaka, Dhaka-1000, Bangladesh;4. Faculty of Information Technology, University of Jyvaskyla, FI-40014 University of Jyvaskyla, Finland |
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| Abstract: | ![]()
Mining closed frequent itemsets from data streams is of interest recently. However, it is not easy for users to determine a proper minimum support threshold. Hence, it is more reasonable to ask users to set a bound on the result size. Therefore, an interactive single-pass algorithm, called TKC-DS (top-K frequent closed itemsets of data streams), is proposed for mining top-K closed itemsets from data streams efficiently. A novel data structure, called CIL (closed itemset lattice), is developed for maintaining the essential information of closed itemsets generated so far. Experimental results show that the proposed TKC-DS algorithm is an efficient method for mining top-K frequent itemsets from data streams. |
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