Authors:
Xiaoliang Geng
1
;
Takeaki Uno
2
and
Hiroki Arimura
1
Affiliations:
1
Hokkaido University, Japan
;
2
National Institute of Informatics, Japan
Keyword(s):
Trajectory Mining, Spatio-temporal Mining, Depth-first Mining Algorithm, Frequent Itemset Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Foundations of Knowledge Discovery in Databases
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining High-Dimensional Data
;
Mining Text and Semi-Structured Data
;
Symbolic Systems
Abstract:
In this paper, we implement recent theoretical progress of depth-first algorithms for mining flock pat-terns (Arimura et al., 2013) based on depth-first frequent itemset mining approach, such as Eclat (Zaki, 2000) or LCM (Uno et al., 2004). Flock patterns are a class of spatio-temporal patterns that 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). We implemented two extension of a basic algorithm, one for a class of closed patterns, called rightward length-maximal flock patterns, and the other with a speed-up technique using geometric indexes. To evalute these extensions, we ran experiments on synthesis datasets. The experiments demonstrate that the modified algorithms with the above extensions are several order of magnitude faster than the original algorithm in most parameter settings.