Semi-supervised clustering method for fusing pairwise constraint and attribute sorting information

Abstract

The invention provides a semi-supervised clustering method for fusing pairwise constraint and attribute sorting information, wherein the method comprises the following steps of: obtaining initial attribute weights according to the pairwise constraint information provided by the user, adding the attribute sorting information provided by the user on the basis of the initial attribute weights to implement semi-supervised clustering, and then selecting clustering results according to the indexes of accuracy. Due to the addition of attribute sorting information on the basis of fused pairwise constraint information, the semi-supervised clustering method for fusing pairwise constraint and attribute sorting information provided by the invention has the advantages that the corresponding attribute weight can be regulated by using attribute sorting while utilizing the learning attribute weight of pairwise constraint, and the clustering result which is obtained by means of the method is more accurate by influencing and promoting the two priori information mutually.

Description

technical field [0001] The invention relates to a clustering method, in particular to a semi-supervised clustering method which combines pairwise constraints and attribute sorting information. Background technique [0002] As an important data mining tool, cluster analysis divides data into several different groups according to a certain similarity measure. Traditional clustering methods do not need to give any prior information, and only divide according to clustering objective indicators, such as inter-cluster density and intra-cluster variance. The segmentation results of such unsupervised clustering methods are often unsatisfactory and difficult to understand. In order to obtain a satisfactory "accurate" division, some researchers have integrated some prior information into unsupervised clustering, and obtained supervised clustering and semi-supervised clustering. [0003] Unlike supervised clustering, semi-supervised clustering can achieve satisfactory results with on...

AI Technical Summary

Problems solved by technology

Although semi-supervised clustering based solely on one of the restrictive information can effectively improve the quality of the results, they do not comprehensively consider the fusion of these two different types of information when processing, resulting in the accuracy and stability of the clustering results. are not high

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