Analysis and Comparison of Uncertain Means Clustering Algorithm

  • Nini ZhangEmail author
  • Lihua Qi
  • Xiaomei Qin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)


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.


Uncertain means clustering K-Means clustering KNN clustering Unsupervised clustering 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Information Electrical EngineeringUniversity of Engineering HebeiHandanChina

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