Heuristic search in restricted memory (research note)
Artificial Intelligence
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Geoinformatica
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ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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ACM Transactions on Information Systems (TOIS)
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Computational & Mathematical Organization Theory
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Data Mining and Knowledge Discovery
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The VLDB Journal — The International Journal on Very Large Data Bases
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Information Sciences: an International Journal
AWA*-a window constrained anytime heuristic search algorithm
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
UP-Growth: an efficient algorithm for high utility itemset mining
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DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Mining maximal groups from spatio-temporal data of mobile users is a well known problem. However, number of such groups mined can be very large, demanding further processing to come up with a readily usable set of groups. In this paper, we introduce the problem of mining a set of K maximal groups which covers maximum number of users. Such a set of groups can be useful for businesses which plan to distribute a set of K offers targeting groups of users such that a large number of users are covered. We propose efficient methods to solve this hard problem, which do not mine the total set of groups apriori (avoiding the large amount of time consumed upfront), instead intelligently decide during their execution as to which of the search space is to be explored to mine the next group so that a set of K groups covering large number of users is quickly produced, and then improve the K-set as time progresses (anytime nature). Experimental results on several synthetic spatio-temporal datasets as well as real datasets that are publicly available show the efficacy and scalability of the proposed methods across various parametric inputs.