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Possibility fuzzy c mean clustering algorithm based on multiple kernels

A possibility, multi-core technology, applied in computing, computer components, character and pattern recognition, etc.

Inactive Publication Date: 2016-08-24
HEFEI UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the deficiencies in the above-mentioned prior art, the present invention proposes a multi-kernel possibility fuzzy clustering algorithm, in order to accurately avoid the problems that FCM is sensitive to noise points and PCM is easy to produce consistent clustering, thereby It can further increase the accuracy of the algorithm, and at the same time find the most suitable weight value and the size of the current membership value, thereby improving the reliability and convergence of the algorithm

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  • Possibility fuzzy c mean clustering algorithm based on multiple kernels
  • Possibility fuzzy c mean clustering algorithm based on multiple kernels
  • Possibility fuzzy c mean clustering algorithm based on multiple kernels

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Embodiment Construction

[0040] In this embodiment, the variant data set and the glass identification data set are used for experimental description. The variant data set has a total of 160 data points, divided into 9 attributes, and a total of eight categories. The glass identification data set has a total of 214 points, divided into 9 There are 6 categories in total.

[0041] A multi-kernel possibility fuzzy clustering algorithm is carried out according to the following steps:

[0042] Step 1. Let X={x 1 ,x 2 ,...,x j ,...,x n} represents a given sample set, x j Indicates the j-th sample; 1≤j≤n, n is the number of samples; optimally divide the sample set X so that the objective function value J shown in formula (1) is the smallest, and the clustering obtained by the smallest objective function value The center is the best, and the effect of dividing the data is also the best. The classification effect is shown in Table 1 and Table 2:

[0043] Table 1 Clustering accuracy results experiment

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Abstract

The invention discloses a possibility fuzzy c mean clustering algorithm based on multiple kernels. The possibility fuzzy c mean clustering algorithm is characterized by comprising the following steps of: 1, performing optimal partition on a sample set in order to minimize a objective function value; 2, acquiring an initial membership matrix and an initialized clustering center; 3, acquiring a membership value, a clustering center, and a typical value by iteration; and 4, acquiring an objective function with an introduced weighted index. The possibility fuzzy c mean clustering algorithm may accurately avoid a problem that FCM is sensitive to noise and is liable to generate consistency clustering so as to be further increased in accuracy, and may discover a most appropriate weighted value and a current membership value so as to be increased in reliability and convergence.

Description

technical field [0001] The invention belongs to an algorithm for data classification in the field of data mining, in particular to a multi-kernel possibility fuzzy clustering algorithm. Background technique [0002] Clustering is a very important branch of unsupervised pattern recognition. The ultimate goal of clustering is to make the distance between similar samples as small as possible and to make the distance between different samples as large as possible. In this way, data can be distinguished. , categorical data. Fuzzy c-means clustering algorithm (FCM) is a basic method for us to study fuzzy clustering. It is a fuzzy clustering algorithm developed by Dunn and developed by Bezdek. The algorithm is mainly based on the concept of minimum square error and stipulates that all samples The sum of the membership degrees is 1, but the membership degree is not consistent with the intuitive membership degree or compatibility degree. When the FCM algorithm performs clustering w...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2321
Inventor 唐益明赵跟陆丰刚永任福继胡相慧
Owner HEFEI UNIV OF TECH
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