A Laplace centric peak data clustering method based on curvature

A technology of Laplacian and data clustering, applied in other database clustering/classification, data mining, database models, etc., can solve problems such as artificially setting parameters

Pending Publication Date: 2019-01-18
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to overcome the problems of the existing clustering algorithms that need to manually set parameters in the clustering process, and at the same time consider improving the performance of the clustering effect, the present invention proposes a method with high accuracy, no parameters, and can automatically determine the number of clusters A Curvature-Based Laplacian Centrality Peak Data Clustering Method

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  • A Laplace centric peak data clustering method based on curvature
  • A Laplace centric peak data clustering method based on curvature
  • A Laplace centric peak data clustering method based on curvature

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[0027] The present invention will be further described below in conjunction with the accompanying drawings.

[0028] refer to figure 1 , a curvature-based Laplacian centrality peak data clustering method, including the following steps:

[0029] Step 1: Preprocess the data set to be classified with n data points, calculate the distance between any two data points, and transform the data set to be classified into a weighted fully coupled network G=(N, E, W), E is a set of edges, V is a set of nodes, and W is a set of weights connecting edges between nodes, where a data point in the original data set corresponds to a node in the network, and the weight of an edge between any two nodes in the network is the distance between the corresponding two data points;

[0030] Step 2: Calculate the sum of the weights of all the edges of each node to obtain a diagonal matrix

[0031]

[0032] in

[0033] Step 3: Calculate the Laplacian matrix L(G)=Y(G)-W(G) of the weighted network G...

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Abstract

A Laplace centroid peak data clustering method based on curvature is disclosed. The data set to be classified is preprocessed to transform the data set to be classified into a weighted fully coupled network, and the Laplace centroid and the minimum distance value of the data points are calculated. The curvature-based method determines the optimal number of clusters R, and then chooses R data points with high Laplacian centrality and distance value as clustering centers. Finally, the remaining nodes are assigned and the clustering is completed. A method based on curvature determines that optimal clustering number, and can automatically find the correct number of clusters without predetermining the number of clusters, so as to realize the true parameterless clustering.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a curvature-based Laplacian centrality peak data clustering method. Background technique [0002] With the development of science and technology and the diversification of people's means of obtaining data, the amount and structure of data possessed by human beings has been greatly improved. How to mine useful information from these data has increasingly become a necessary technology. . Traditional data analysis is to access and simply operate the data stored in the database. The amount of information contained in the data we obtain through this method is only a small part of the amount of information contained in the entire database. Hidden in these The more important information behind the data is the description of the overall characteristics of the data and the prediction of its development trend, which has important reference value in the process of decision-making. This creates ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/28G06F16/906
CPCG06F2216/03
Inventor 杨旭华金林波
Owner ZHEJIANG UNIV OF TECH
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