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accurate clusters even in the case of the failure of
classical clustering algorithms.
5 CONCLUSIONS
Inspired by an optimization model based on discrete
Morse theory, we propose the new clustering
framework that is mainly to adopt to the thought of
hierarchical clustering based on kernel density
estimation. The experimental results on some
synthetic and UCI data sets have demonstrated that
the new algorithm can discover clusters with
arbitrary shapes and densities at different levels,
moreover it can successfully divide data points
overlapping to the feature space into many correct
clusters. The results show the feasibility and
effectiveness of the new clustering algorithm.
ACKNOWLEDGEMENTS
This work is carried out under the “Taishan Scholar”
project of Shandong China. It is also supported in
part by the Natural Science Foundation of China
(61472231 , 61640201) and the Scientific and
Technology Project of Shandong Education Bureau.
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