Mining frequent spatio-temporal sequential patterns

Many applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatiotemporal series, we study the problem of discovering sequential patterns, which are routes frequently followed by the object. Sequential pattern mining algorithms for transaction data are not directly applicable for this setting. The challenges to address are: (i) the fuzziness of locations in patterns, and (ii) the identification of non-explicit pattern instances. In this paper, we define pattern elements as spatial regions around frequent line segments. Our method first transforms the original sequence into a list of sequence segments, and detects frequent regions in a heuristic way. Then, we propose algorithms to find patterns by employing a newly proposed substring tree structure and improving a priori technique. A performance evaluation demonstrates the effectiveness and efficiency of our approach.

[1]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[2]  E. R. White.  Assessment of Line-Generalization Algorithms Using Characteristic Points , 1985 .

[3]  J. Hershberger,et al.  Speeding Up the Douglas-Peucker Line-Simplification Algorithm , 1992 .

[4]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[5]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[6]  Eamonn J. Keogh,et al.  An online algorithm for segmenting time series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[7]  Dimitrios Gunopulos,et al.  Efficient Mining of Spatiotemporal Patterns , 2001, SSTD.

[8]  Philip S. Yu,et al.  Mining long sequential patterns in a noisy environment , 2002, SIGMOD '02.

[9]  Eamonn J. Keogh,et al.  Finding surprising patterns in a time series database in linear time and space , 2002, KDD.

[10]  Jiawei Han,et al.  Mining top-k frequent closed patterns without minimum support , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[11]  Eamonn J. Keogh,et al.  Clustering of streaming time series is meaningless , 2003, DMKD '03.

[12]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

[13]  George Kollios,et al.  Mining, indexing, and querying historical spatiotemporal data , 2004, KDD.

[14]  Heikki Mannila,et al.  Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.