Abstract
Clustering analysis is an important method of multivariate statistical analysis. It has important applications in pattern recognition, artificial intelligence, automatic control and other fields. An iterative algorithm called uncertain means clustering is defined by analyzing the contribution of the features to the sample and calculating the degree of membership based on the weight. In this paper, we use the uncertain means clustering algorithm to cluster IRIS data to test the clustering accuracy, convergence speed and robustness of the algorithm. At the same time, compared with the traditional clustering algorithm, which K-Means clustering algorithm and KNN clustering algorithm, the experimental results show that the uncertain means clustering algorithm has good performance in the accuracy and convergence speed of the sample data sets, and is an effective unsupervised clustering algorithm.
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Zhang, N., Qi, L., Qin, X. (2019). Analysis and Comparison of Uncertain Means Clustering Algorithm. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds) Geo-informatics in Sustainable Ecosystem and Society. GSES 2018. Communications in Computer and Information Science, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-13-7025-0_9
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DOI: https://doi.org/10.1007/978-981-13-7025-0_9
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