Entropy weight-based global K-means clustering method

Abstract

The invention relates to an entropy weight-based global K-means clustering method. The method is realized through the steps of: firstly, clustering data into a class, wherein the optimal clustering center is the centroid of all data; next, determining the minimum data point of an objective function through computation, and taking the data point as an initial clustering center of a next class; then iterating and updating by utilizing an entropy weight-based K-means method to get the optimal clustering centers when the data are clustered into two classes; and sequentially increasing the number of the clustering centers by adopting the same method to iterate and update until the clustering of the set K class is completed, and in this way, completing the whole process of clustering all data points into the K class. According to the method provided by the invention, the global K-means clustering method is combined with K-means with entropy attributes, a new entropy weight value-based global K-means clustering method is constructed, and the clustering results are quite stable; and compared with the experimental results of varieties of K-means clustering methods, the effectiveness and the robustness of the clustering method provided by the invention are proven.

Description

technical field [0001] The present invention relates to a new clustering method. Specifically, a global K-means clustering method based on entropy weights is proposed to solve the problems of low clustering accuracy and unstable clustering results of common K-means methods in clustering. While improving the clustering accuracy of the method, a very stable clustering result is obtained. Background technique [0002] Clustering is a process of dividing a group of samples into each class, so that the intra-class distance is minimized and the inter-class distance is maximized, that is, samples in the same class are as similar as possible, and samples in different classes are as similar as possible. different. Clustering plays an important role in data mining, statistics, machine learning, spatial database technology, biology, and marketing. [0003] In recent years, data has become increasingly complex in many application domains of clustering. A target is often described by...

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Problems solved by technology

[0012] The technical problem to be solved by the present invention is that the K-means method of entropy weight is used alone and has poor stability. In order to improve the accuracy of multidimensional data clustering and enhance the stability of clustering

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