Abstract
Domain experts are frequently interested to analyze multiple related spatial datasets. This capability is important for change analysis and contrast mining. In this paper, a novel clustering approach called correspondence clustering is introduced that clusters two or more spatial datasets by maximizing cluster interestingness and correspondence between clusters derived from different datasets. A representative-based correspondence clustering framework and clustering algorithms are introduced. In addition, the paper proposes a novel cluster similarity assessment measure that relies on re-clustering techniques and co-occurrence matrices. We conducted experiments in which two earthquake datasets had to be clustered by maximizing cluster interestingness and agreement between the spatial clusters obtained. The results show that correspondence clustering can reduce the variance inherent to representative-based clustering algorithms, which is important for reducing the likelihood of false positives in change analysis. Moreover, high agreements could be obtained by only slightly lowering cluster quality.
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Ding, W., Jiamthapthaksin, R., Parmar, R., Jiang, D., Stepinski, T., Eick, C.F.: Towards Region Discovery in Spatial Datasets. In: Proceedings of 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2008)
Eick, C.F., Parmar, R., Ding, W., Stepinki, T., Nicot, J.-P.: Finding Regional Co-location Patterns for Sets of Continuous Variables in Spatial Datasets. In: Proceedings of 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2008)
Marx, Z., Dagan, I., Buhmann, J.M., Shamir, E.: Coupled Clustering: A Method for Detecting Structural Correspondence. Journal of Machine Learning Research 3, 747–780 (2002)
Dhillon, I.S.: Co-clustering Documents and Words using Bipartite Spectral Graph Partitioning. In: Proceedings of 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)
Cheng, Y., Church, C.M.: Biclustering of Expression Data. In: Proceedings of 8th International Conference on Intelligent Systems for Molecular Biology (2000)
Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary Clustering. In: Proceedings of 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006)
Chen, C.S., Rinsurongkawong, V., Eick, C.F., Twa, M.D.: Change Analysis in Spatial Data by Combining Contouring Algorithms with Supervised Density Functions. In: Proceedings of 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2009)
Rand, W.: Objective Criteria for the Evaluation of Clustering Methods. Journal of the American Statistical Association 66, 846–850 (1971)
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Rinsurongkawong, V., Eick, C.F. (2010). Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_25
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DOI: https://doi.org/10.1007/978-3-642-13657-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13656-6
Online ISBN: 978-3-642-13657-3
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