Abstract
The traditional hierarchical methods always fail to take both the features of connectivity and proximity of clusters into consideration at the same time. This paper presents a hierarchical clustering algorithm based on cluster outline, which effectively addresses clusters of arbitrary shapes and sizes, and is relatively resistant to noise and easily detects outliers. The definition of the boundary point and cluster outline is firstly given, and the standard and approach of measuring similarity between clusters is then taken with the feature of connectivity and proximity of clusters. The experiments on the Iris and image data sets confirm the feasibility and validity of the algorithm.
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Acknowledgments
The research is founded in part by The Natural Science Foundation of Inner Mongolia under Grant No. 2012MS0611, Chunhui Project of Ministry of Education under Grant No. Z2009-1-01041, and Higher School Science Research Project of Inner Mongolia under Grant No. NJZZ11140.
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Meng, HD., Ren, JP., Song, YC. (2014). Research on Hierarchical Clustering Algorithm Based on Cluster Outline. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Soft Computing Techniques and Engineering Application. Advances in Intelligent Systems and Computing, vol 250. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1695-7_1
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DOI: https://doi.org/10.1007/978-81-322-1695-7_1
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