Adaptive semi-supervised density clustering method and system

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...

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.

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