Abstract
An algorithm for optimizing data clustering in feature space is studied in this work. Using graph Laplacian and extreme learning machine (ELM) mapping technique, we develop an optimal weight matrix W for feature mapping. This work explicitly performs a mapping of the original data for clustering into an optimal feature space, which can further increase the separability of original data in the feature space, and the patterns points in same cluster are still closely clustered. Our method, which can be easily implemented, gets better clustering results than some popular clustering algorithms, like k-means on the original data, kernel clustering method, spectral clustering method, and ELM k-means on data include three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, and Wine database).
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The IRIS data can be downloaded from the following address: http://archive.ics.uci.edu/ml/datasets/Iris
Wisconsin’s breast cancer database can be downloaded from the following address: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)
Wine database can be downloaded from the following address: https://archive.ics.uci.edu/ml/datasets/Wine
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Acknowledgments
This research was supported by Natural Science Foundation of China under Grant No. 11171137, Zhejiang Provincial Natural Science Foundation of China under Grant No. LY13A010008, and Scientific Research Fund of Zhejiang Provincial Education Department under Grant No. 2014.
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Xie, L., Lu, C., Mei, Y. et al. An optimal method for data clustering. Neural Comput & Applic 27, 283–289 (2016). https://doi.org/10.1007/s00521-014-1818-3
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DOI: https://doi.org/10.1007/s00521-014-1818-3