Optimization of covariance distance measurement algorithm for multidimensional clustering analysis
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Abstract:
In order to use effective distance measurement methods to characterize the proximity of data objects in multi-dimensional clustering analysis, a covariance distance measurement (CDM) algorithm is proposed. First, fuzzy C-means (FCM) is used to assign weights to the data objects, so that the membership degree of each sample point relative to the category feature is obtained. Based on the membership degree, the difference degree of each sample is calculated. Then, as the first optimization criterion, the variance distance measure is used to replace the Euclidean distance measure in fuzzy clustering to make similar data objects closer. Finally, the covariance distance measure between the sample points is used as the second optimization criterion to make the different data objects separate from each other. The optimal solution is calculated iteratively with alternate fixed variables, so that the clustering index and distance measurement learning parameters are optimized at the same time, and better clustering results are obtained. Experimental results on different data sets show that compared with FCM-Sig and UNCA algorithms, CDM algorithm has better performance in clustering accuracy and algorithm convergence.
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Supported by National Natural Science Foundation of China(61761025) and Major Science and Technology Project of Yunnan Province(202002AD080002).
