%0 Conference Paper %1 1565665 %A Cao, Huiping %A Mamoulis, N. %A Cheung, D.W. %D 2005 %J Data Mining, Fifth IEEE International Conference on %K spatio-temporal-mining %P 8 pp.- %R 10.1109/ICDM.2005.95 %T Mining frequent spatio-temporal sequential patterns %X 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.

@inproceedings{1565665, abstract = { 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.}, added-at = {2009-05-29T14:01:26.000+0200}, author = {Cao, Huiping and Mamoulis, N. and Cheung, D.W.}, biburl = {https://www.bibsonomy.org/bibtex/2ae7e03b2f044cb3e97342fe9c2099201/claudio.lucchese}, description = {Welcome to IEEE Xplore 2.0: Mining frequent spatio-temporal sequential patterns}, doi = {10.1109/ICDM.2005.95}, interhash = {1c4009cdf438266f784a446dd9f6d00b}, intrahash = {ae7e03b2f044cb3e97342fe9c2099201}, issn = {1550-4786}, journal = {Data Mining, Fifth IEEE International Conference on}, keywords = {spatio-temporal-mining}, month = {Nov.}, pages = { 8 pp.-}, timestamp = {2009-05-29T14:01:26.000+0200}, title = {Mining frequent spatio-temporal sequential patterns}, year = 2005 }