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Adaptive semi-supervised density clustering method and system

A density clustering, semi-supervised technique applied in the field of data processing to reduce side effects

Inactive Publication Date: 2018-01-09
YUNNAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] On the singleness of algorithm parameters,
[0011] The choice of algorithm parameters is generally single. For the same algorithm, no set of parameters can be universally applicable to complex cluster structures presented by various types of data sets.

Method used

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  • Adaptive semi-supervised density clustering method and system
  • Adaptive semi-supervised density clustering method and system
  • Adaptive semi-supervised density clustering method and system

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Embodiment Construction

[0049] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0050] In the self-adaptive semi-supervised density clustering method provided by the present invention, when the data used for clustering has characteristics such as cluster sizes and shapes, the density-based clustering algorithm is a very ideal choice. Unlike the segmentation-based clustering algorithm, it needs to work hard to construct the optimal segmentation of the global data space, such as the DBSCAN algorithm to achieve the optimal region. Furthermore, the density-based semi-supervised learning algorithm can use both Must-Link constraints and Cannot-Link constraints for the data instances that are closest to each ...

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Abstract

The invention belongs to the technical field of data processing, and discloses an adaptive semi-supervised density clustering method and system. First, density parameters are automatically extracted from data with and without class labels; The initial clustering analysis is performed to obtain the local clustering results; finally, the final global clustering results are obtained by integrating the local clustering results. The algorithm proposed by the present invention does not need to set the number of clusters. In the cluster analysis process, the number of clusters is automatically determined according to the data set density information; Automatically extract multiple sets of density parameters, and then use these density parameters to perform density-based cluster analysis on the target data set, which can obtain excellent cluster analysis results, and has strong adaptability and noise resistance.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to an adaptive semi-supervised density clustering method and system. Background technique [0002] Clustering algorithm can be said to be one of the most important data mining tasks in computer operation, and it is dedicated to the structure of clusters in the target data. A cluster consists of instances that are "similar" to each other, and instances that are "dissimilar" are in other clusters. Under different angles and different standards, the classification of clustering algorithms appears to be various. However, there is a classification system for clustering algorithms that is recognized by everyone. The system divides clustering algorithms into hierarchical clustering, partitioning-based clustering, density-based clustering, and model-based clustering. -based). Recently, many researchers try to extract some constraint information from supervision infor...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 杨云李宗泽
Owner YUNNAN UNIV
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