Fuzzy C-means clustering method of minimum variance optimization initial cluster center

An initial clustering center and initial clustering technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem that the result cannot get the optimal solution
CN107330458AInactive Publication Date: 2017-11-07CHANGZHOU COLLEGE OF INFORMATION TECH
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Patent Information

Authority / Receiving Office
CN ยท China
Current Assignee / Owner
CHANGZHOU COLLEGE OF INFORMATION TECH
Publication Date
2017-11-07
Estimated Expiration
Not applicable ยท inactive patent

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Abstract

The invention discloses a fuzzy C-means clustering method of a minimum variance optimization initial cluster center, and belongs to the technical field of data mining and pattern recognition. The method comprises the steps of clustering a distance relation between an input data set and sample points; using a clustering analysis method for clustering analysis of a target data set to obtain a clustering label; and evaluating the performance of the clustering label obtained after the clustering analysis and an original label according to an evaluation index. The invention aims to solve the problem that a clustering effect of the fuzzy C-means is greatly affected by an initialized clustering center thereof and an optimal solution cannot be guaranteed. Based on the FCM algorithm, the selection of the initial clustering centers is first performed by selecting K sample points with the least variance on different regions as the initial cluster centers by taking the sample variance as heuristic information and by the sample field radius, and the algorithm does not need the setting of any parameters.
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Description

technical field

[0001] The invention relates to a clustering analysis method of a data set, in particular to a fuzzy C-means clustering method for optimizing an initial clustering center with minimum variance, and belongs to the technical field of data mining and pattern recognition. Background technique

[0002] The traditional FCM algorithm selects the cluster centers randomly, which easily leads to unstable clustering results, and may even make the cluster centers converge to local extreme values. To solve the above problems, according to the compactness information of the sample distribution , the initial clustering center can be optimized according to the minimum variance. The initialization algorithm calculates the variance of the sample according to the spatial distribution information of the sample to obtain the closeness information of the sample, and selects the sample point with the minimum variance and the sample point within a certain range as the initial cluster...

Claims

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