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A Clustering Method Based on Extended Entropy Information Bottleneck Theory

An information bottleneck and clustering method technology, applied in the field of data mining, can solve problems such as unsupervised clustering analysis

Inactive Publication Date: 2011-12-28
SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Since there is no prior target as a reference, this problem is an unsupervised clustering analysis problem

Method used

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  • A Clustering Method Based on Extended Entropy Information Bottleneck Theory
  • A Clustering Method Based on Extended Entropy Information Bottleneck Theory
  • A Clustering Method Based on Extended Entropy Information Bottleneck Theory

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Embodiment Construction

[0025] In order to better understand the present invention, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0026] Clustering is the process of dividing a data set into several classes by analyzing the correlation between variables, so that the differences within a class are small and the differences between classes are large. The most important thing in the clustering process is how to measure the correlation between data sets, and to give clustering an objective clustering standard.

[0027] The present invention proposes a clustering method based on the extended entropy information bottleneck theory, and the specific operation method of the method is introduced as follows:

[0028] information bottleneck theory

[0029] First introduce the information bottleneck theory. Given a target set, the bottleneck-based clustering method seeks to minimize the information loss between target classes an...

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Abstract

The invention discloses a clustering method based on the extended entropy information bottleneck theory, which is mainly aimed at the unsupervised clustering problem in data mining, when the relationship between clustering arrays is complex and cannot be described by statistical probability. , this method can not only reflect the complex correlation between the arrays, but also reflect the corresponding relationship between the corresponding positions of the arrays, and the clustering method can provide an objective clustering censorship criterion, effectively avoiding subjectively specifying the number of clusters number of defects. The clustering method can be widely used in clustering problems in fields such as medicine, intelligent transportation, and pattern recognition.

Description

technical field [0001] The invention relates to the field of data mining, in particular to cluster analysis. Background technique [0002] Cluster analysis is an important research content in the field of data mining, which aims to divide the data set into several classes, so that the differences within the class are small and the differences between classes are large. The distance between data is usually used to describe the similarity, the larger the distance, the smaller the similarity, and vice versa. Cluster analysis is widely used in pattern recognition, data analysis, image processing, intelligent transportation, medicine and other fields. Many effective clustering methods have been formed, such as K-means method, k-center method, self-organizing neural network, Bayesian neural network, Fisher clustering and so on. Different clustering methods have different starting points, which are mainly reflected in the measurement of the distance between samples or variables. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 孙占全
Owner SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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