Rapid clustering method based on density sub-graph estimation, computer equipment and storage medium

A technology of density subgraph and clustering method, applied in computer parts, computing, instruments, etc., can solve problems such as high computational cost and inability to determine the centroid of clusters, achieving the effect of less computation and avoiding over-segmentation

Active Publication Date: 2021-01-01
GUANGDONG UNIV OF TECH
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Problems solved by technology

[0004] The present invention provides a fast clustering method based on density subgraph estimation, computer equipment and storage to overcome the de

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  • Rapid clustering method based on density sub-graph estimation, computer equipment and storage medium
  • Rapid clustering method based on density sub-graph estimation, computer equipment and storage medium
  • Rapid clustering method based on density sub-graph estimation, computer equipment and storage medium

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Embodiment 1

[0040]This embodiment proposes a fast clustering method based on density subgraph estimation, such asfigure 1 As shown, this embodiment is a flowchart of a fast clustering method based on density subgraph estimation.

[0041]The fast clustering method based on density subgraph estimation proposed in this embodiment specifically includes the following steps:

[0042]S1: Obtain samples and preprocess the samples to form a data set.

[0043]The data set in this embodiment includes two types, one is real world data set samples, and the other is picture samples. For the real world data set samples, they can be downloaded directly from the website, such as the UCI website (a database for machine learning proposed by the University of California Irvine); the image samples first obtain the image samples that need to be segmented online, Preprocess the picture again.

[0044]The specific steps of preprocessing the picture sample include: converting the picture into a 5-dimensional array, where each poin...

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Abstract

The invention relates to the technical field of machine learning, and provides a rapid clustering method based on density sub-graph estimation, computer equipment and a storage medium in order to overcome the defects that in the prior art, the mass center of a cluster cannot be determined, the calculation cost is high, and over-segmentation occurs in the clustering process. The rapid clustering method based on density sub-graph estimation comprises the following steps: acquiring samples, and preprocessing the samples to form a data set; performing density value estimation on each sample in thedata set, and constructing a density sub-graph set; finding out the highest density point of each density sub-graph from the density sub-graph set to serve as a representative point of the density sub-graph, and forming a candidate set by samples corresponding to the representative points; calculating an important value of each sample in the candidate set; sorting the candidate sets in a descending order according to the important values, and selecting the first K samples as the centroids of the K clusters; and classifying non-centroid samples in the candidate set, and outputting to obtain aclustering result.

Description

Technical field[0001]The present invention relates to the technical field of machine learning, and more specifically, to a fast clustering method, computer equipment and storage medium based on density subgraph estimation.Background technique[0002]The density-based clustering method is a classic research direction in data mining. It is very popular in academia and industry because it can find clusters of any shape in the data set, mainly through kernel density estimates (KDE, kernel density estimates) Density joins the samples of the data set.[0003]In the traditional density-based clustering method, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) mainly divides the clusters by judging whether the density of the sample points is reachable. However, DBSCAN cannot determine the centroid of the cluster, and the centroid is usually used as the representative point of the cluster. In response to the above problems, MeanShift proposes that for each sample point, the ke...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23
Inventor 杨易扬郑喜臣任成森巩志国蔡瑞初郝志峰陈炳丰
Owner GUANGDONG UNIV OF TECH
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