Author:
de Amorim Renato Cordeiro,Komisarczuk Peter
Publisher
Springer Berlin Heidelberg
Reference19 articles.
1. Ball, G.H., Hall, D.J.: A clustering technique for summarizing multivariate data. Behavioral Science 12(2), 153–155 (1967)
2. MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, California, USA, pp. 281–297 (1967)
3. Chan, E.Y., Ching, W.K., Ng, M.K., Huang, J.Z.: An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recognition 37(5), 943–952 (2004)
4. Huang, J.Z., Ng, M.K., Rong, H., Li, Z.: Automated variable weighting in k-means type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 657–668 (2005)
5. Huang, J.Z., Xu, J., Ng, M., Ye, Y.: Weighting Method for Feature Selection in K-Means. In: Computational Methods of feature selection, pp. 193–209. Chapman & Hall (2008)
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Initializing FWSA K-Means With Feature Level Constraints;IEEE Access;2022
2. New K-means Clustering Method Using Minkowski’s Distance as its Metric;British Journal of Computer, Networking and Information Technology;2021-07-06
3. FPKC: An Efficient Algorithm for Improving Short-Term Load Forecasting;Advances in Intelligent Information Hiding and Multimedia Signal Processing;2019-07-10
4. The LINEX Weighted k-Means Clustering;Journal of Statistical Theory and Applications;2019
5. LINEX K-Means: Clustering by an Asymmetric Dissimilarity Measure;Journal of Statistical Theory and Applications;2018