ABSTRACT
Spatio-temporal frequent patterns discovered from historical trajectories of moving objects can provide important knowledge for location-based services. To address the problem of finding sequential patterns from spatio-temporal datasets, continuous values of spatial and temporal attributes should be discretized with the minimum loss of information. Since data carries spatio-temporal correlation among attributes, it should be preserved during discretization to derive accurate patterns. In this paper, we define the problem of discretizing spatio-temporal data and propose a discretization method preserving spatio-temporal correlations in the data. Using line simplification, our method first abstracts trajectories into approximations considering the distributions of input data and then clusters them into logical cells. We experimentally analyze the effectiveness of the proposed approach in reducing the size of data and improving efficiency of the mining processes.
- Tsoukatos, I. and Gunopulos, D. 2001. Efficient mining of spatiotemporal patterns. In Proceedings of International Symposium on in Spatial and Temporal Databases (Redondo Beach, CA, July 2001). 425--442 Google ScholarDigital Library
- Dougherty, J., Kohavi, R. and Sahami, M. 1995. Supervised and unsupervised discretization of continuous features. In Proceedings of 12th International Conference on Machine Learning (Tahoe City, CA, 1995), 194--202Google Scholar
- Hussain, F., Liu, H., Tan, C. L. and M. Dash. 2002. Discretization: An Enabling Technique. Journal of Data Mining and Knowledge Discovery, 6, 4(June, 2002) 393--423 Google ScholarDigital Library
- Mehta, S., Parthasarathy S. and Yang, H. 2005. Towards unsupervised correlation preserving discretization, IEEE Transactions on Knowledge and Data Engineering. 17, 9(Sep. 2005), 1174--1185 Google ScholarDigital Library
- Yavas, G., Katsaros, D., Ulusoy, O. and Manolopoulos. Y. 2005. A data mining approach for location prediction in mobile environments, Data and Knowledge Engineering. 54, 2, (Aug. 2005), 121--146 Google ScholarDigital Library
- Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y. and Cheung, D. W. 2000. Mining, indexing, and querying historical spatiotemporal data. In Proceedings of 10th International Conference on Knowledge Discovery and Data Mining KDD (Seattle, WA, Aug. 2004). 236--245 Google ScholarDigital Library
- Cao, H., Mamoulis, N. and Cheung, D. W. 2005. Mining frequent spatio-temporal sequential patterns. In Proceedings of Data Mining (Houston, Texas, Nov. 2005). 82--89 Google ScholarDigital Library
- Cao, H., Wolfson, O. and Trajcevski. G. 2006. Spatio-temporal data reduction with deterministic error bounds. The VLDB Journal, 15, 3 (Sep. 2006), 221--228 Google ScholarDigital Library
- Hershberger, J. and Snoeyink. J. 1992. Speeding up the Douglas-Peucker line-simplification algorithm. In Proceedings of 5th International Symposium on Data Handling (Charleston, SC, Aug. 1992). 134--143Google Scholar
- Zhang, T., Ramakrishnan, R. and Livny. M. 1996. BIRCH: An efficient data clustering method for very large databases. In Proceedings ACM SIGMOD Conference on Management of Data (Montreal, Cananda, June, 1996). 103--114 Google ScholarDigital Library
- Tzouramanis, T., Vassilakopoulos, M. and Manolopoulos. Y. 2002. On the generation of time-evolving regional data. Geoinformatica, 6, 3 (Sep. 2002), 207--231 Google ScholarDigital Library
- Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U. and Hsu. M. C. 2001. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of 17th International Conference on Data Engineering (Heidelberg, Germany, April, 2001). 215--224 Google ScholarDigital Library
Index Terms
- Spatio-temporal discretization for sequential pattern mining
Recommendations
From sequential pattern mining to structured pattern mining: A pattern-growth approach
AbstractSequential pattern mining is an important data mining problem with broad applications. However, it is also a challenging problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. ...
Partial spatio-temporal co-occurrence pattern mining
Spatio-temporal co-occurrence patterns represent subsets of object-types that are often located together in space and time. The aim of the discovery of partial spatio-temporal co-occurrence patterns (PACOPs) is to find co-occurrences of the object-types ...
Efficient STMPM (Spatio-Temporal Moving Pattern Mining) Using Moving Sequence Tree
NCM '08: Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 02Recently, based on the dynamic location or mobility of a moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for the development of location based services. The ...
Comments