A Non Parametric Approach to the Outlier Detection in Spatio–Temporal Data Analysis
Detecting outliers which are grossly different from or inconsistent with the remaining spatio–temporal data set is a major challenge in real-world knowledge discovery and data mining applications. In this paper, we face the outlier detection problem in spatio–temporal data. The proposed non parametric method rely on a new fusion approach able to discover outliers according to the spatial and temporal features, at the same time: the user can decide the importance to give to both components (spatial and temporal) depending upon the kind of data to be analyzed and/or the kind of analysis to be performed. Experiments on synthetic and real world data sets to evaluate the effectiveness of the approach are reported.
- 1.D. Birant, A. Kut, “Spatio-Temporal Outlier Detection in Large Databases”, Journal of Computing and Information Technology, vol. 14, no. 4, pp. 291–297, 2006.Google Scholar
- 2.T. Cheng, Z. Li, “A Multiscale Approach for Spatio-Temporal Outlier Detection”, Transactions in GIS, vol. 10, no. 2, pp. 253–263, march 2006.Google Scholar
- 3.E. M. Knorr, T.Ng. Raymond, “A Unified Notion of Outliers: Properties and Computation”, 3 rd International Conference on Knowledge Discovery and Data Mining Proceedings, pp. 219–222, 1997.Google Scholar
- 4.E. Knorr and R. Ng, Algorithms for Mining Distance-Based Outliers in Large Datasets, Proc. Intl Conf. Very Large Databases (VLDB 98), pp. 392–403, 1998.Google Scholar
- 5.Ng RT, Han J. Efficient and Effective Clustering Methods for Spatial Data Mining, In: Proc. 20th Int. Conf. on Very Large Data Bases, Santiago, Chile; 1994. p. 144–155.Google Scholar