Abstract
The quantities of earth science data collected have necessitated the development of new data mining tools and techniques. Mining this data can produce new insights into weather, climatological and environmental trends that have significance both scientifically and practically. This paper discusses the challenges posed by earth science databases and examines the use of fuzzy K-Means clustering for analyzing such data. It proposes the extension of the fuzzy K-Means clustering algorithm to account for the spatio-temporal nature of such data. The paper introduces an unsupervised fuzzy clustering algorithm, based on the fuzzy K-Means and defines a cluster validity index which is used to determine an optimal number of clusters. It is shown experimentally that the algorithms are able to identify and preserve regions of meteorological interest
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References
Gahegan, M.: Data Mining and Knowledge Discovery in the Geographical Domain: Intersection of Geospatial Information and Information Technology. National Academies, Computer Science and Telecommunications Board (2002)
Roddick, J.F., Spiliopoulou, M.: A Bibliography of Temporal, Spatial, and Spatio-Temporal Data Mining Research. SIGKDD Explorations 1(1) (1999)
Openshaw, S.: The Modifiable Areal Unit Problem. CATMOG 38 (1984)
Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: 4th International Symposium on Large Spatial Databases (SSD 1995), Maine (1995)
Ester, M., Kriegel, H.-P., et al.: A Density Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996) (1996)
Steinbach, M., Tan, P.N., et al.: Clustering Earth Science Data: Goals, Issues and Results. In: Fourth KDD Workshop on Mining Scientific Datasets (2001)
Gahegan, M., Wachowicz, M., et al.: The Integration of Geographic Visualization with Knowledge Discovery in Databases and Geocomputation. Cartography and Geographic Information Systems (2001)
Mohan, B.K.: Integration of IRS-1A L2 data by fuzzy logic approaches for landuse classification. International Journal of Remote Sensing 21(8), 1709–1713 (2000)
Gasch, A.P., Eisen, M.B.: Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology Research, 0059.1–0059.22 (2002)
Guler, C., Thyne, G.D., et al.: Evaluation of graphical and multivariate statistical methods for classification of water chemistry data. Hydrogeology Journal 10(4), 455–474 (2002)
Baker, N., et al.: The Navy Operational Global Atmospheric Prediction System: A Brief History of Past, Present, and Future Developments (1998)
Forgy, E.: Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometry 21(785) (1965)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symp. on Probability and Statistics, Berkeley (1967)
deGruijter, J.J., McBratney, A.B.: A modified fuzzy k means for predictive classification. In: Bock, H.H. (ed.) Classification and Related Methods of Data Analysis, pp. 97–104. Elsevier Science, Amsterdam (1988)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Cosic, D., Loncaric, S.: New methods for cluster selection in unsupervised fuzzy clustering. In: Proceedings of the 41th Anniversary Conference of KoREMA (1996)
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Liu, Z., George, R. (2003). Fuzzy Cluster Analysis of Spatio-Temporal Data. In: Yazıcı, A., Şener, C. (eds) Computer and Information Sciences - ISCIS 2003. ISCIS 2003. Lecture Notes in Computer Science, vol 2869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39737-3_122
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DOI: https://doi.org/10.1007/978-3-540-39737-3_122
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20409-1
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