K-means clustering method based on quotient space theory

A k-means clustering and quotient space technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of high time complexity, achieve good clustering results and good overall effect.

Inactive Publication Date: 2014-05-21
XIAN UNIV OF TECH
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[0005] The purpose of the present invention is to provide a kind of K-means clustering method based o

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  • K-means clustering method based on quotient space theory
  • K-means clustering method based on quotient space theory
  • K-means clustering method based on quotient space theory

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Embodiment Construction

[0042] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] Relevant theorem among the present invention, definition are as follows:

[0044] Definition (Granularity) Granularity refers to the degree of refinement and comprehensiveness of data in a dataset. The principle of granularity division is: the higher the degree of refinement, the smaller the granularity; the lower the degree of refinement, the larger the granularity.

[0045] Define X as the domain of discourse of the problem to be studied, f as the attribute function on the domain of discourse, and T as the structure of the domain of discourse, and describe the problem by constructing a triple (X, f, T).

[0046] Theorem 1 (False Guaranteed Principle) If the problem A→B has a solution on (X,f,T), then on the quotient space ([X],[f],[T]), the problem [A]→[B ] There must also be a solution.

[0047] Theorem 2 (fidelity principle I) I...

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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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Application Information

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IPC IPC(8): G06F17/30
CPCG06F18/23213
Inventor 周红芳张国荣刘园郭杰段文聪王心怡何馨依
Owner XIAN UNIV OF TECH
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