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Self-adaptive multi-mean two-step clustering method

A clustering method and mean clustering technology, applied in the field of data clustering, can solve the problems of sensitivity to outliers and high algorithm complexity, and achieve the effect of small influence of outliers, low complexity, and elimination of the influence of sensitive outliers.

Inactive Publication Date: 2022-05-06
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

However, there are also disadvantages of high algorithm complexity and sensitivity to outliers.

Method used

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  • Self-adaptive multi-mean two-step clustering method
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  • Self-adaptive multi-mean two-step clustering method

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Embodiment

[0051] In the adaptive multi-means two-step clustering method of this embodiment, for the input data, the first step adopts the multi-means clustering algorithm based on chaotic quantum particle swarm optimization for preliminary clustering, and the second step adopts the adaptive hierarchical clustering algorithm for further clustering based on the clustering results of the first step to obtain the final clustering results; The flow chart of clustering method is as follows: Figure 1 The algorithm diagram is shown in Figure 2 The specific steps are as follows:

[0052] Step 1: multi mean clustering algorithm based on chaotic quantum particle swarm optimization; The flow chart of multi mean clustering algorithm is shown in Fig Figure 3 As shown in Figure 1.1, initialize clustering radius C r And allowable deviation C d , randomly select a data X i As the initial classification, the centroid of the classification is determined, and the clustering radius C is determined by the method...

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Abstract

The invention relates to an adaptive multi-mean two-step clustering method, and the method comprises the steps: carrying out the preliminary clustering of input data through employing a multi-mean clustering algorithm based on a chaotic quantum particle swarm in the first step, and carrying out the further clustering on the basis of a clustering result in the first step through employing an adaptive hierarchical clustering algorithm in the second step, and obtaining a final clustering result; the method can be used for cluster distribution data clustering, can also be used for non-cluster data clustering, and has the advantages of being high in operation speed, low in complexity, wide in application range and small in influence of abnormal values. The method can be used as a basic technology for data processing, and can be used for data processing work in the fields of system modeling, pattern recognition, machine learning, data mining and the like.

Description

technical field [0001] The invention relates to an adaptive multi mean two-step clustering method, which belongs to the technical field of data clustering. Background technology [0002] Data clustering methods can be divided into partition based clustering methods, hierarchy based clustering methods, density based clustering methods and so on. [0003] The clustering method based on division is divided according to the principle of "the similarity of data points within the class is high enough and the similarity of data points between classes is low enough". The classical k-means algorithm is a typical clustering algorithm based on division. K-means algorithm is simple and practical. However, it also has the disadvantages of "the number of clusters K needs to be manually specified, the initial selection of cluster centers has a great impact on the clustering results, is sensitive to outliers, and is unable to deal with the clustering of non clustered distribution data". [0004]...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/00G06N10/60
CPCG06N3/006G06N10/00G06F18/231G06F18/22G06F18/23213
Inventor 董泽姜炜董贺宁常修全
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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