Abstract
Hierarchical K-means clustering is one of important clustering task in data mining. In order to address the problem that the time complexity of the existing HK algorithms is high and most of algorithms are sensitive to noise, a hierarchical K-means clustering algorithm based on silhouette and entropy(HKSE) is put forward. In HKSE, the optimal cluster number is obtained through calculating the improved silhouette of the dataset to be clustered, so that time complexity can be reduced from O(n2) to O(k × n). Entropy is introduced in the hierarchical clustering phase as the similarity measurement avoiding distance calculation in order to reduce outlier effect on the cluster quality. In the post processing phase, the outlier cluster is identified by computing the weighted distance between clusters. Experiment results show that HKSE is efficient in reducing time complexity and sensitivity to noise.
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Dong, W., Ren, J., Zhang, D. (2011). Hierarchical K-Means Clustering Algorithm Based on Silhouette and Entropy. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23881-9_45
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DOI: https://doi.org/10.1007/978-3-642-23881-9_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23880-2
Online ISBN: 978-3-642-23881-9
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