Abstract
Spatial data clustering plays an important role in numerous fields. Data clustering algorithms have been developed in recent years. K-means is fast, easily implemented and finds most local optima. IDBSCAN is more efficient than DBSCAN. IDBSCAN can also find arbitrary shapes and detect noisy points for data clustering. This investigation presents a new technique based on the concept of IDBSCAN, in which K-means is used to find the high-density center points and then IDBSCAN is used to expand clusters from these high-density center points. IDBSCAN has a lower execution time because it reduces the execution time by selecting representative points in seeds. The simulation indicates that the proposed KIDBSCAN yields more accurate clustering results. Additionally, this new approach reduces the I/O cost. KIDBSCAN outperforms DBSCAN and IDBSCAN.
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References
Xu, R., Wunsch, D.: Survey of Clustering Algorithm. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
McQueen, J.B.: Some Methods of Classification and Analysis of multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Zhang, T., Ramakrishnan, R., Livny, M.: An efficient Data Clustering Method for Very Large Data Bases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, vol. 25(2), pp. 103–114 (1996)
Guha, S., Rastogi, R., Shim, K.: An Efficient Clustering Algorithm for Large Data Bases. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, vol. 27(2), pp. 73–84 (1998)
Guha, S., Rastogi, R., Shim, K.: ROCK: A Robust Clustering Algorithm for Categorical Attributes. In: Proceedings of 15th International Conference on Data Engineering, pp. 512–521 (1999)
Karypis, G., Han, E.H., Kumar, V.: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. IEEE Computers 32(8), 68–75 (1999)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Borah, B., Bhattacharyya, D.K.: An Improved Sampling-Based DBSCAN for Large Spatial Databases. In: Proceedings of International Conference on Intelligent Sensing and Information, pp. 92–96 (2004)
Wang, W., Yang, J., Muntz, R.: STING: A Statistical Information Grid Approach to Spatial Data Mining. In: Proceedings of 23rd International Conference on Very Large Data Bases, pp. 186–195 (1997)
Wang, W., Yang, J., Muntz, R.: STING+: An approach to Active Spatial Data Mining, Technical report, UCLA CSD, No. 980031 (1998)
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© 2006 Springer-Verlag Berlin Heidelberg
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Tsai, CF., Liu, CW. (2006). KIDBSCAN: A New Efficient Data Clustering Algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_73
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DOI: https://doi.org/10.1007/11785231_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35748-3
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