Jiaji Qiu
ABSTRACT
This paper proposes a method that reduces the intra-cluster distance and increases the inter-cluster distance in the k-means problem with missing data. Filling in missing data, calculating intra-cluster distances between clusters, and clustering problems are integrated into one function, and solved through loop iterations. Finally, the method is applied to 4 UCI datasets, and the results show that the method has good effect.
- Jain, A. K. 2010. Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.Google Scholar
- Feng, J., Zhang, Y., Yue, G., Liu, X., Su, H., & Zhang, P. F. 2018. Atherosclerotic Plaque Pathological Analysis by Unsupervised $ K $-Means Clustering. IEEE Access, 6, 21530-21535.Google ScholarCross Ref
- Munir, M. U., Javed, M. Y., & Khan, S. A. 2012. A hierarchical k-means clustering based fingerprint quality classification. Neurocomputing, 85, 62-67.Google ScholarDigital Library
- Peng, K., Leung, V. C., & Huang, Q. 2018. Clustering approach based on mini batch kmeans for intrusion detection system over big data. IEEE Access, 6, 11897-11906.Google ScholarCross Ref
- Lin, X., & Li, C. T. 2016. Large-scale image clustering based on camera fingerprints. IEEE Transactions on Information Forensics and Security, 12(4), 793-808.Google Scholar
- Wang, S., Li, M., Hu, N., Zhu, E., Hu, J., Liu, X., & Yin, J. 2019. K-means clustering with incomplete data. IEEE Access, 7, 69162-69171.Google ScholarCross Ref
- Wu, S., & Chow, T. W. 2004. Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. Pattern Recognition, 37(2), 175-188.Google ScholarCross Ref
- García-Laencina, P. J., Sancho-Gómez, J. L., Figueiras-Vidal, A. R., & Verleysen, M. 2009. K nearest neighbours with mutual information for simultaneous classification and missing data imputation. Neurocomputing, 72(7-9), 1483-1493.Google ScholarDigital Library
- Aste, M., Boninsegna, M., Freno, A., & Trentin, E. 2015. Techniques for dealing with incomplete data: a tutorial and survey. Pattern Analysis and Applications, 18(1), 1-29.Google ScholarDigital Library
- Dempster, A. P., Laird, N. M., & Rubin, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22.Google ScholarCross Ref
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