Global K-means clustering method based on feature weight

Abstract

A global K-means clustering method based on a feature weight is disclosed. The implementation process of the method comprises the following steps of: firstly, clustering data into one class, wherein the optimal clustering centre is the centre of mass of all the data; secondly, figuring out the minimal data point of a target function through calculation and taking the data point as the initial clustering centre of a next class; thirdly, performing iterative updating by a K-means method with the feature weight to obtain the optimal clustering centre of clustering into two classes; and finally, performing updating iteration by orderly increasing the number of the clustering centres by the same method until the clustering of K classes predetermined is completed, thus completing the whole process of clustering all the data points into K classes. In the invention, the new global K-means clustering method based on the feature weight is established by combining the global K-means method with the K-means with the feature weight, and the clustering result is very stable; and in contrast with the experimental results of several K-means clustering methods, the effectiveness and the robustness of the clustering method disclosed by the invention are proved.

Description

technical field [0001] The invention belongs to the field of data statistics and relates to a clustering method. Specifically, a global K-means clustering method based on feature weight is proposed, which is used to solve the problem of unstable clustering results of the common K-means methods in clustering, and improves the clustering accuracy of the method. At the same time, a very stable clustering result is obtained. Background technique [0002] Clustering is a process of dividing a set of data into various classes, so that the intra-class distance is minimized and the inter-class distance is maximized, that is, the data in the same class are as similar as possible, while the data 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 clu...

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Problems solved by technology

[0012] The technical problem to be solved by the present invention is, in order to improve the accuracy of multi-dimensional data clustering and enhance the stability of clustering results, aiming at the problem of relatively poor stability of the LAW-K-means method in use, for the multi-dimensional data clustering characteristics, using the LAW-K-means method has better clustering results and the global K-means has the characteristics of stable results, combining the two, a global K-means clustering method based on feature weights is proposed, which can obtain Higher clustering accuracy and stability

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