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
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
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 ...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 


