Abstract
Based on the theory of data field, each sample point in the spatial database radiates its data energy from the sample space to the mother space. This paper studies the use of the data field as a basis for clustering. We put forward a novel method for clustering, which is a kind of natural clustering method called spatial neighborhood clustering. In the data field, the potential center is identical to the cluster center. The key step of the cluster algorithm is to find the potential centers in the grid units of data field. The spatial neighborhood cluster method makes use of the distribution property of the potential value point as the potential center in the data field to discriminate the maximum potential value in a certain neighborhood window. Then the cluster centers can be acquired corresponding to the maximum potential values and the number of cluster centers is automatically amount to that of potential centers.
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References
Deren, L., Shuliang, W., Deyi, L.: Spatial Data Mining Theories and Applications. Science Press, China (2006)
Ester, M., et al.: A density—based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, USA (1996)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data 1996, pp. 103–114 (1996)
Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, New York, NY, pp. 73–84 (1998)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of International Conference on Very Large Databases, Santiago, Chile, pp. 487–499 (1994)
Fotheringham, A., Peter, R.: Spatial Analysis and GIS. Taylor and Francies, Abington (1994)
Tung, H., Jean, H., Jiawei, H.: Spatial clustering in the presence of obstacles. In: IEEE Transactions on Knowledge and Data Engineering, pp. 359–369 (2001)
Murray, T., Shyy, K.: Integrating attribute and space characteristics in choropleth display and spatial data mining. International Journal of Geographical Information Science 14(7), 649–667 (2000)
Usama, F., Gregory, P., Padhraic, S., Ramasamy, U.: Advances in knowledge Discovery and Data Mining. AAA/MIT Press, Menlo Park (1996)
Ming-Syan, C., Jiawei, H., Philip, Y.: Data mining: an overview from database perspective. IEEE Transactions on Knowledge and Data Engineering (1997)
Leonard, K., Peter, R.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Chichester (1990)
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Fang, M., Wang, S., Jin, H. (2010). Spatial Neighborhood Clustering Based on Data Field. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_25
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DOI: https://doi.org/10.1007/978-3-642-17316-5_25
Publisher Name: Springer, Berlin, Heidelberg
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