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Improved multipath clustering method

A clustering method and clustering technology, applied in instruments, character and pattern recognition, computer components and other directions, can solve the problems of not considering multipath attributes, relying on the selection of local optimal solutions, etc., to reduce time complexity and improve The effect of classification accuracy and accurate clustering results

Inactive Publication Date: 2017-03-29
TIANJIN UNIV
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Problems solved by technology

However, this algorithm does not consider the influence of multipath attribute differences on multipath weighting factors, and the algorithm still has defects such as being heavily dependent on the selection of initial cluster centers and easily falling into local optimal solutions.

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Embodiment Construction

[0055] The improved KPowerMeans algorithm proposed by the present invention determines the number of multipath clusters and the initial cluster center by wavelet peak detection technology before multipath clustering, effectively overcomes the sensitivity of the KPowerMeans algorithm to the initial cluster center, and then according to the multipath Based on the different contributions of attributes to classification, the principle of information entropy is used to weight the distance of multipath components to improve the classification accuracy.

[0056] Aiming at the above-mentioned problems in the existing algorithms, the present invention proposes an improved multipath clustering algorithm, which is based on the KPowerMeans algorithm. First, the peak detection technique of wavelet transform is used instead of random selection. This supervised clustering algorithm can overcome the sensitivity to the initial cluster center, thus obtaining a stable clustering effect. Next, b...

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Abstract

The invention belongs to the field of data mining. Based on different contributions made to classification by properties of multipaths, classification precision can be improved after multipath component distance is subjected to characteristic weighting operation via use of an information entropy principle. A technical solution adopted in the invention is that an improved multipath clustering method comprises the following steps: wavelet transformation peak detection technologies are used for replacing random selection, and stable clustering effects can be obtained; based on consideration of effects exerted by multipath power, the information entropy principle is introduced for calculating multipath component distance (MCD) of multipath property self-adaption weighting, and different multipaths can be assigned to different clusters according to different MCD. The improved multipath clustering method is mainly applied to data processing occasions.

Description

technical field [0001] The invention belongs to the fields of data mining, pattern recognition, unsupervised learning in machine learning, channel modeling and channel characteristic research, and specifically relates to an improved multipath clustering method. Background technique [0002] Clustering algorithm is a very important data analysis method in data mining. The goal of this algorithm is to divide a large amount of data in the data set into different clusters, so that the difference between each data in the same cluster is as small as possible, and different clusters are different. The difference between the internal data is as large as possible, so that the data can be analyzed for application and actual research content. [0003] The clustering algorithm has a long history. As early as 1967, MacQueen proposed the KMeans clustering algorithm, which uses the sum of the squares of Euclidean distances of different data as the objective function. Afterwards, Hartigan p...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 杨晋生赵月秋吴旭曌陈为刚
Owner TIANJIN UNIV
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