Gauss induction kernel based fuzzy c-means clustering algorithm

A mean clustering and fuzzy technology, applied in computing, computer components, character and pattern recognition, etc., can solve problems such as poor clustering performance, achieve accurate clustering performance, and improve optimization performance

Active Publication Date: 2017-10-13
CHANGZHOU INST OF TECH
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Problems solved by technology

The third is to use the particle swarm optimization algorithm (PSO) to search for the cluster center in the input space, which is called the PSO kernel fuzzy c-means clustering algor

Method used

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  • Gauss induction kernel based fuzzy c-means clustering algorithm
  • Gauss induction kernel based fuzzy c-means clustering algorithm
  • Gauss induction kernel based fuzzy c-means clustering algorithm

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Embodiment Construction

[0035] In this embodiment, the algorithm comparison test is performed based on the public data set in the UCI machine learning database. The selected data set is the Iris data set, and the information of the data set is shown in Table 1. The HKFCM, GKFCM algorithm and Gauss-induced kernel fuzzy c-means clustering algorithm (hereinafter referred to as GIKFCM algorithm) are selected for comparative testing.

[0036] Table 1 iris experimental data set

[0037]

[0038] The Gauss-induced kernel fuzzy c-means clustering algorithm is carried out as follows:

[0039] Step 1: Let X={x 1 ,x 2 ,L,x j ,L,x n} represents a given sample set, x j Indicates the jth sample; 1≤j≤n, n is the number of samples; optimally divide the sample set X so that the objective function value J KFCM minimum, where J KFCM Determined by formula (1). The test results of GIKFCM algorithm, GKFCM algorithm, and HKFCM algorithm are shown in Table 2, Table 3, and Table 4, respectively.

[0040] During t...

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Abstract

The invention discloses a Gauss induction kernel based fuzzy c-means clustering algorithm, which comprises the steps of 1, performing optimized classification on a sample set according to an objective function minimization principle; 2, initializing a fuzzy membership degree or initializing a clustering center; 3, performing parameter estimation on the fuzzy membership degree and the clustering center according to an iterative calculation formula in a Gauss induction kernel clustering algorithm; and 4, acquiring an optimized objective function. The clustering performance of a kernel clustering algorithm can be improved by effectively utilizing the nonlinear expression ability of a kernel method in the clustering algorithm. The clustering center iteration formula does not contain the clustering center itself, conditions of the clustering algorithm for iterating a convergence proof are met, and thus the convergence of the algorithm is ensured theoretically.

Description

technical field [0001] The invention belongs to an algorithm for unsupervised data classification in the field of data mining, in particular to a fuzzy c-means clustering algorithm based on a Gauss induced kernel. Background technique [0002] Cluster analysis is an important part of unsupervised pattern recognition. The purpose of clustering is to group similar samples together and divide dissimilar samples into different categories. Fuzzy c-means clustering algorithm (FCM) is the most widely used method in cluster analysis. It is a fuzzy clustering algorithm developed by Dun, Bezdek and others. The FCM algorithm is based on the weighted error square sum minimization theory , using the Euclidean distance to measure the sample and the cluster center, which is used to express the error between the sample and the cluster center, which is suitable for data with a linear relationship in the data set, but the clustering of nonlinear data is often not effective. [0003] Since th...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/232
Inventor 文传军陈荣军刘福燕
Owner CHANGZHOU INST OF TECH
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