Abstract
This paper proposes a new algorithm, Slice_OP, which selects the initial cluster centers on high-dimensional data. A set of observation points is allocated to transform the high-dimensional data into one-dimensional distance data. Multiple Gamma models are built on distance data, which are fitted with the expectation-maximization algorithm. The best-fitted model is selected with the second-order Akaike information criterion. We estimate the candidate initial centers from the objects in each component of the best-fitted model. A cluster tree is built based on the distance matrix of candidate initial centers and the cluster tree is divided into K branches. Objects in each branch are analyzed with k-nearest neighbor algorithm to select initial cluster centers. The experimental results show that the Slice_OP algorithm outperformed the state-of-the-art Kmeans++ algorithm and random center initialization in the k-means algorithm on synthetic and real-world datasets.
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Acknowledgment
This paper was supported by National Natural Science Foundations of China (under Grant No. 61473194 and 61472258) and Shenzhen-Hong Kong Technology Cooperation Foundation (under Grant No. SGLH20161209101100926).
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Masud, M.A., Huang, J.Z., Zhong, M., Fu, X., Mahmud, M.S. (2018). Slice_OP: Selecting Initial Cluster Centers Using Observation Points. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_2
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