MGKFCM (multipath Gauss kernel fuzzy c-means clustering algorithm)

Abstract

The invention discloses an MGKFCM (multipath Gauss kernel fuzzy c-means clustering algorithm). The MGKFCM comprises the following steps: 1, performing optimized division on a sample set according to the principle of minimization of an MGKFCM objective function; 2, initializing clustering centers and calculating fuzzy membership and an objective function value 1 with a gradient iterative formula; 3, estimating the clustering centers and calculating the fuzzy membership and an objective function value 2 with PSO (particle swarm optimization); 4, selecting a cluster center with smaller objective function value as an iteration path on the basis of the objective function value 1 and the objective function value 2; 5, obtaining the MGKFCM objective function through calculation. Two Gaussian kernel clustering algorithm iteration paths including a gradient method and the PSO are integrated, the path with the smaller clustering objective function value is taken as the parameter iteration path, optimal performance of the two algorithms is used effectively, and the clustering performance of the clustering algorithm is improved.

Description

technical field [0001] The invention belongs to an algorithm for unsupervised data classification in the field of data mining, in particular to a multi-path Gaussian kernel fuzzy c-mean clustering algorithm. Background technique [0002] The fuzzy clustering method based on the objective function has the advantages of describing the fuzzy relationship between categories, expressing the clustering problem in mathematical form, and nonlinear programming optimization theory, etc., so it has become the mainstream of research in the field of clustering analysis. Typical algorithms of this type of algorithm include fuzzy c-means clustering (FCM), possibility c-means clustering (PCM) and generalized algorithms, which are widely used in image processing, pattern recognition, computer vision and other fields. [0003] The kernel method uses the kernel function to represent the inner product operation in the high-dimensional feature space, which can project the nonlinear relationship ...

AI Technical Summary

Problems solved by technology

Wu Zhongdong et al. introduced the kernel method into the fuzzy c-means clustering algorithm to obtain the kernel fuzzy c-means clustering algorithm (KFCM), which uses the gradient method and the kernel clustering objective function to obtain Cluster center Φ(v i ), but due to the unknowability of the nonlinear mapping function Φ(·), it is compared with the nonlinear mapping sample Φ(x j ) and feature space cluster center Φ(v i ) for inner product operation and expressed by kernel function, so that Φ(v i ) is hidden in the kernel clustering algorithm in an implicit way, so this kind of kernel clustering algorithm is called hidden kernel fuzzy c-means clustering algorithm (hidden kernel fuzzy c-means clustering algorithm: HKFCM), HKFCM algorithm is vulnerable to fuzzy indicators The effect of clustering is poor

Images