Abstract
With the rapid development of the technology of cluster analysis, people have proposed a lot of clustering algorithms, such as the K-means clustering algorithm which is simple, low complexity and has been used widely, and it has been the improved object or base for many other algorithms. This paper presents a K-means optimization algorithm based on relative core cluster -RCBK-means. The algorithm is based on the core group, uses the center of the relative core cluster of the data set as the initial center of the K-means algorithm, thus avoiding the local optimization problem of the clustering results which caused by selecting the initial center randomly of the classic K-means algorithm, and improving the algorithm results effectively.
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References
Chen, J.: High-dimensional clustering knowledge discovery key technology research and application, p. 21. Electronics Industry Press (2009)
Ester, M.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD 1996, pp. 226–231 (1996)
Ankerst, M.: OPTICS: ordering points to identify the clustering structure. In: ACM SIGMOD International Conference on Management of Data Press, pp. 49–60 (1999)
Strehl, A.: Relationship-based clustering and cluster ensembles for high-dimensional data mining (2002); Lingzhu, H., et al.: Locally Linear Embedding Algorithm with Adaptive Neighbors. In: International Workshop on Intelligent Systems and Applicationss, pp. 1–4 (2009)
MacQueen, J.: Some methods for classification and analysis of multivariate observations, pp. 68–75 (2002)
Fan, H.: Introduction to Data Mining. People’s Posts and Telecommunications Press, Beijing (2006)
Huang, S., Li: Based on Adaptive nearest neighbor clustering fusion method. Computer Engineering and Applications
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Liu, G., Huang, S., Chang, H. (2012). A K-Means Optimization Algorithm Based on Relative Core Cluster. In: Zhang, Y. (eds) Future Communication, Computing, Control and Management. Lecture Notes in Electrical Engineering, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27314-8_52
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DOI: https://doi.org/10.1007/978-3-642-27314-8_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27313-1
Online ISBN: 978-3-642-27314-8
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