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Clustering center selection method, device and medium

A clustering algorithm and clustering technology, applied in the field of clustering analysis, can solve problems such as long calculation cycle, high calculation time complexity, and affecting the execution efficiency of clustering algorithms

Inactive Publication Date: 2018-09-14
GUANGDONG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The current method for determining the cluster center is usually to calculate the local density of each sample point and the distance from each sample point to a point with a higher local density. Since the number of sample points is relatively large, it is calculated when selecting the cluster center The time complexity is high, and the calculation cycle is relatively long, which will affect the execution efficiency of the clustering algorithm

Method used

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  • Clustering center selection method, device and medium
  • Clustering center selection method, device and medium
  • Clustering center selection method, device and medium

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Embodiment 2

[0066] On the basis of the above embodiments, the present invention also provides the following series of preferred implementation manners.

[0067] figure 2 It is a flowchart of another method for selecting a cluster center provided by an embodiment of the present invention. figure 2 In steps S11-S12 and figure 1 Same, no more details here.

[0068] Such as figure 2 As shown, as a preferred implementation manner, step S10 specifically includes:

[0069] Step S20: Calculate the Euclidean distance between each sample point

[0070] Among them, i is the dimension of the sample point, n is an integer constant greater than or equal to 1, and x i is the coordinate of the first sample point in the i dimension, y i is the coordinate of the second sample point in the i dimension.

[0071] It should be noted that since the number of dimensions of the sample points is greater than or equal to 1, when calculating the Euclidean distance of two sample points, it is necessary to...

Embodiment 3

[0085]The embodiment of the method for selecting the cluster center has been described in detail above, and the present invention also provides a device for selecting the cluster center corresponding to the method, because the embodiment of the device part and the embodiment of the method part are mutually Correspondingly, for the embodiment of the device part, please refer to the description of the embodiment of the method part, and details will not be repeated here.

[0086] image 3 A structural diagram of a device for selecting a cluster center provided by an embodiment of the present invention. The selection device of the cluster center provided by the embodiment of the present invention includes:

[0087] The acquiring device 10 is configured to acquire the local density of each sample point in the sample set and the high-density distance between each sample point and a sample point with a higher local density through multithreading set by OpenMp in parallel.

[0088] ...

Embodiment 4

[0103] The present invention also provides a device for selecting a cluster center, which is applied to a density peak clustering algorithm, including:

[0104] memory for storing computer programs;

[0105] A processor, configured to implement the steps of the above-mentioned method for selecting a cluster center when executing a computer program.

[0106] The selection device of the clustering center provided by the present invention obtains the local density of each sample point in the sample set in parallel through the multi-threading set by OpenMp, and the high-density distance between each sample point and a sample point with a higher local density. The local density and the high-density distance are used as operation parameters, and the density threshold and the high-density distance threshold are respectively calculated according to the preset rules, and then the local density and the density threshold are selected in the sample set to satisfy the first preset relation...

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Abstract

The invention discloses a clustering center selection method. The method includes the following steps that: the local densities of sample points in a sample set and high density distances between eachsample point and sample points with higher local densities are obtained on the basis of a plurality of threads set by the OpenMp; on the basis of preset rules, a density threshold is calculated withthe local densities adopted as parameters, and a high density distance threshold is calculated with the high density distances adopted as parameters; a target sample point of which the local density and the density threshold satisfy a first preset relationship and of which the high-density distance and the high-density distance threshold satisfy a second preset relationship is selected from the sample set and is adopted as a clustering center. With the clustering center selection method of the invention adopted, the time complexity of calculation is reduced, a calculation period is correspondingly reduced, and therefore, the execution efficiency of a clustering algorithm is ensured. The present invention also provides a clustering center selection device and a medium. The clustering centerselection device and the medium have the same advantages as the above method.

Description

technical field [0001] The invention relates to the field of cluster analysis, in particular to a method, device and medium for selecting a cluster center. Background technique [0002] Clustering analysis, referred to as clustering, is a process of dividing data objects into subsets. Each subset is a cluster, and after dividing the data objects, the objects in the cluster are similar to each other and not similar to the objects in other clusters. Among many cluster analysis, the density peak clustering algorithm is a relatively simple implementation. This algorithm is a new type of clustering algorithm that can cluster non-spherical data sets. It needs to calculate the local density of the sample point and the sample point to The distance of points with higher local density is used to determine the cluster center among many sample points, and then cluster the sample points. [0003] The current method for determining the cluster center is usually to calculate the local de...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23
Inventor 邱安波王卓薇
Owner GUANGDONG UNIV OF TECH
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