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Course information determination method and device based on K-means algorithm, equipment and storage medium

A k-means algorithm and technology for determining methods, applied in the field of clustering, can solve problems such as inaccurate classification results of training course information

Pending Publication Date: 2020-07-31
CHINA PING AN LIFE INSURANCE CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Embodiments of the present invention provide a method, device, device, and storage medium K-means for determining course information based on the K-means algorithm, so as to solve the inconsistencies in the classification results of the application tool training course information for users to be trained in specific scenarios in the prior art. exact question

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  • Course information determination method and device based on K-means algorithm, equipment and storage medium
  • Course information determination method and device based on K-means algorithm, equipment and storage medium
  • Course information determination method and device based on K-means algorithm, equipment and storage medium

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Embodiment 1

[0037] figure 1 A flow chart showing the method for determining course information based on the K-means algorithm in this embodiment. The method for determining course information based on the K-means algorithm can be applied to various terminals, wherein the terminals can be computer devices such as desktop computers, notebooks, palmtop computers, and cloud servers, and are not limited here.

[0038] based on figure 1 In the illustrated embodiment, by taking the usage habit data of all users' application tools as sample data, and using the density-based clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to classify the sample data, we can obtain Clustering data clusters of the sample data, and removing the discrete sample data of the clustering data clusters to obtain the first data cluster, that is, the first data cluster does not include the discrete sample data determined after clustering, and the first data cluster The clusters are...

Embodiment 2

[0089] Figure 7 A functional block diagram of a device for determining course information based on the K-means algorithm corresponding to the method for determining course information based on the K-means algorithm in Embodiment 1 is shown. Specifically, such as Figure 7 As shown, the device for determining course information based on the K-means algorithm includes an acquisition module 10 , a first clustering module 20 , a K value acquisition module 30 , a second clustering module 40 and a course information determination module 50 . Wherein, the implementation functions of the acquisition module 10, the first clustering module 20, the K value acquisition module 30, the second clustering module 40 and the course information determination module 50 correspond to the course information determination method based on the K-means algorithm in Embodiment 1 The steps correspond to each other one by one, and to avoid redundant description, this embodiment does not describe them in...

Embodiment 3

[0115] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for determining course information based on the K-means algorithm in Embodiment 1 is implemented, in order to avoid duplication , which will not be repeated here. Alternatively, when the computer program is executed by the processor, the functions of the modules, sub-modules, and units in the K-means algorithm-based course information determination device in Embodiment 2 are implemented. To avoid repetition, details are not repeated here. It can be understood that the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal and telecommunication ...

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Abstract

The invention discloses a course information determination method, device and equipment based on a K-means algorithm and a storage medium, and the method comprises the steps: obtaining the use habit data of application tools of all users, and enabling the use habit data of the application tools to serve as sample data; clustering the sample data by adopting a density-based clustering algorithm DBSCAN to obtain a clustering data cluster of the sample data, and removing discrete sample data of the clustering data cluster to obtain a first data cluster; and taking the first data cluster as inputdata of a K-means algorithm, taking a preset K value as a K value of the K-means algorithm, and clustering the first data cluster according to the K-means algorithm to obtain a target clustering result. According to the course information determination method based on the K-means algorithm, the density-based clustering algorithm DBSCAN is combined, and part of outliers are eliminated, so that theinfluence of the outliers can be reduced when the initial clustering center point and the initial iteration center point are selected by the K-means algorithm, and the clustering effect can be effectively improved.

Description

technical field [0001] The present invention relates to the technical field of clustering, in particular to a method, device, equipment and storage medium for determining course information based on a K-means algorithm. Background technique [0002] Nowadays, with the emergence of various application tools, especially the use of technology application tools by technology companies is becoming more and more frequent and there are many types, such as different drawing tools and different versions of drawing tools. Application tools vary in their ability to adapt. In the traditional processing scheme, in order to improve the tool application ability of users in enterprises or companies, K-means clustering is often used for cluster analysis based on the user's usage habit data to group users according to the user's tool application ability. The goal of clustering is to make the similarity of objects of the same class as large as possible; the similarity between objects of diffe...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q50/20
CPCG06Q50/20G06F18/2321G06F18/23213G06F18/24
Inventor 黄跃鹏
Owner CHINA PING AN LIFE INSURANCE CO LTD