K-means clustering method based on quotient space theory

Abstract

Provided is a K-means clustering method based on a quotient space theory. The method comprises the steps that firstly, a cluster number K and a data set X are input, then the data set is clustered, and finally a clustering result is output. The method has the good clustering result on class clusters in any shape and noise points, the clustering effect is far better than that of a K-means algorithm, the time performance of the method is far better than that of an MSCA algorithm, the time performance and the clustering effect are combined, and the overall effect is best.

Description

technical field [0001] The invention belongs to the technical field of data mining methods, and relates to a K-means clustering method based on quotient space theory. Background technique [0002] In the field of data mining, cluster analysis is an important research topic. Clustering technology has been widely used in telecommunications, retail, biology, marketing and other fields. Clustering is an unsupervised classification, the purpose of which is to find the data points in the data set that are clustered due to the characteristics of the object itself, and to ensure that the similarity within the cluster is as large as possible, and the similarity between clusters is as large as possible. Different degrees. Existing clustering algorithms are generally divided into: 1. Partition-based clustering algorithms represented by K-means, Fuzzy K-means, and k-center points; 2. Hierarchical clustering algorithms represented by CURE, BIRCH, and ROCK 3. Density-based clustering a...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a kind of K-means clustering method based on quotient space theory, solve the problem that the time complexity of existing technology is too high

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