Efficient Mining of Length-Maximal Flock Patterns from Large Trajectory Data
نویسندگان
چکیده
In this paper, we study the problem of mining a class of spatio-temporal patterns, called flock patterns, which represent a groups of moving objects close each other in a given time segment (Gudmundsson and van Kreveld, Proc. ACM GIS’06; Benkert, Gudmundsson, Hubner, Wolle, Computational Geometry, 41:11, 2008). Based on frequent-pattern mining approach, such as Apriori, Eclat, or LCM, we present an efficient depth-first mining algorithm that finds all maximally longest flock patterns appearing in a collection of trajectory data in polynomial time per pattern using polynomial space, without using table-lookup. We also present a geometic pruning technique which significantly improves the efficiency of the algorithm very much.
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