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Density-based weighted fuzzy C-means clustering method

A mean clustering and density technology, applied in character and pattern recognition, instruments, electrical digital data processing, etc., can solve problems such as low accuracy

Inactive Publication Date: 2021-06-22
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0005] In view of this, the present invention mainly solves the problem of low accuracy of the traditional fuzzy C-means clustering method along with the diversity of data

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  • Density-based weighted fuzzy C-means clustering method

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Embodiment Construction

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings for the implementation of the present invention. Obviously, the described examples are only part of the implementation examples of the present invention, rather than all embodiments. Embodiments, and all other embodiments obtained by persons of ordinary skill in the art without creative efforts, all belong to the scope of protection of the present invention.

[0023] like figure 1 As shown, the present invention provides a kind of density-based weighted fuzzy C-means clustering method, and its basic implementation process is as follows:

[0024] 1. Input data preprocessing.

[0025] Input data set D = {x 1 ,x 2 ,...,x n}∈R S .

[0026] 2. Use the density-based clustering method to complete the screening of initial cluster centers.

[0027] First use a density-based method to calculate each data object x i...

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Abstract

The invention relates to a density-based weighted fuzzy C-means clustering method, belongs to the field of data mining data object clustering, and aims to solve the problems of a traditional FCM clustering algorithm in practical application. The traditional FCM clustering algorithm directly processes data in a data set and calculates an objective function of the data, and solves a membership function and a clustering center function through a target function, so that the clustering precision is not high, and the requirements of actual application cannot be met. In order to solve the problem, a density-based thought is introduced, and an FCM clustering algorithm is improved, so that the improved algorithm can improve the clustering precision. According to the method, the influence of noise on data clustering can be avoided, the method has relatively high noise anti-interference capability, and the clustering efficiency is effectively improved.

Description

technical field [0001] The invention belongs to the application field of computer technology and relates to a density-based weighted fuzzy C-mean clustering method. Background technique [0002] In recent years, with the rapid development of data mining technology, data mining technology has been widely used in many fields, such as economy, environment, medical finance and other fields. Clustering is an unsupervised analysis method without prior conditions in data mining. It divides the data in the data set into different clusters according to their respective characteristics. The clusters are as different as possible. The data in the clusters as similar as possible. There are many kinds of clustering methods. The classic clustering methods include partition-based clustering methods, density-based clustering methods, grid-based clustering methods, hierarchical-based clustering methods and model-based clustering methods. [0003] The most classic method in the partition-bas...

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Application Information

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IPC IPC(8): G06F21/60G06K9/62
CPCG06F21/602G06F18/232
Inventor 李媛洁万静王言言
Owner HARBIN UNIV OF SCI & TECH