Clustering analysis method based on sample density and self-adaptive adjustment of clustering center

A self-adaptive adjustment and clustering center technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as restrictive effects, difficult to determine the cost, and large influence of the initial value of the clustering result

Inactive Publication Date: 2020-07-07
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

However, the K-means clustering algorithm needs to manually determine the K value of the clustering category number in advance, and the clustering result is greatly affected by the initial value, which limits its role in solving practical problems to a certain extent.
[0003] When using the K-means algorithm to process data sets, the number of clusters is often not known in advance, and the estimated number of clusters needs to be obtained based on prior knowledge or analysis of

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  • Clustering analysis method based on sample density and self-adaptive adjustment of clustering center
  • Clustering analysis method based on sample density and self-adaptive adjustment of clustering center
  • Clustering analysis method based on sample density and self-adaptive adjustment of clustering center

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Embodiment Construction

[0057] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0058] The invention improves the traditional K-means algorithm from two aspects of selecting a suitable initial cluster center and adaptively adjusting the number of clusters. First, the number of clusters K and the cluster center are initialized based on the sample density and sample neighborhood, and then the method of nearest neighbor cluster merging is used to adaptively adjust the number of clusters K, and finally the best clustering result is obtained.

[0059] A kind of cluster analysis method based on sample density and self-adaptive adjustment cluster center is characterized in that comprising the following steps:

[0060] (1) Select the initial cluster center

[0061] Step1: Initialize the number of clusters Initialize the center point set

[0062] Step2: Dataset X={x for clustering processing 1 ,x 2 ,...,x i ,...,x n}, each sample object con...

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Abstract

The invention relates to a clustering analysis method based on sample density and self-adaptive adjustment of a clustering center. Firstly, a clustering number K and the clustering center are initialized based on the sample density and the sample neighborhood, then the clustering number K is adaptively adjusted by adopting a nearest neighbor cluster merging method, and finally, the optimal clustering result is obtained, so that the effectiveness of the clustering analysis result is greatly improved.

Description

technical field [0001] The invention relates to the field of unsupervised learning cluster analysis in machine learning, in particular to a cluster analysis method based on sample density and self-adaptive adjustment of cluster centers. Background technique [0002] At present, the most widely researched and applied is the clustering method based on partitioning. The K-means algorithm is a typical clustering algorithm based on the idea of ​​partitioning and a clustering algorithm based on distance. The characteristics of fast speed and strong local search ability have been studied by many experts and scholars at home and abroad for many years, and have been widely used in many industrial and commercial fields. However, the K-means clustering algorithm needs to manually determine the K value of the clustering category number in advance, and the clustering result is greatly affected by the initial value, which limits its role in solving practical problems to a certain extent. ...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23211G06F18/2321
Inventor 张维马志华
Owner NORTHWESTERN POLYTECHNICAL UNIV
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