Abstract
K-Means is a powerful clustering method, in which the Euclidean distance is usually employed. In the paper, by following the basic idea of locality preserving projection (LPP), we first define locality preserving scatter matrix, then introduce a new Mahalanobis distance by using the defined matrix, finally propose a novel K-Means clustering algorithm based on the given Mahalanobis distance. Different from the traditional K-Means algorithm, the proposed method considers fully the intrinsic manifold structure of data. Experimental results show that the proposed method can achieve better clustering accuracy in contrast with the traditional K-Means algorithm.
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Acknowledgments
This work is supported by the Graduate Innovation Fund of Xihua University (Grant No.ycjj2014032), the Key Scientific Research Foundation of Sichuan Provincial Department of Education (Grant No.11ZA004) and the National Science Foundation of China (Grant No. 61103168, 61271413, 61472329).
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Yang, X., Wang, X., Tian, Y., Du, Y. (2015). Locality Preserving Based K-Means Clustering. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_9
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DOI: https://doi.org/10.1007/978-3-319-23862-3_9
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