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

A technology of semi-supervised clustering and constraint information, which is applied in the field of semi-supervised clustering that fuses pairwise constraints and attribute ranking information, can solve problems such as failure to comprehensively consider fusion problems, low accuracy and stability of clustering results, and achieve Effect of Accurate Clustering Results

Inactive Publication Date: 2011-05-04
QINGDAO TECHNOLOGICAL UNIVERSITY
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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 comprehensive

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

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Embodiment Construction

[0014] In order to make the object, technical solution and advantages of the present invention more clearly, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0015] The invention provides a semi-supervised clustering method. The method first fuses instance layer information in the form of pair constraints, and learns initial attribute weights. Then, on the basis of satisfying the pairwise constraints as much as possible, continue to add attribute layer information in the form of attribute ranking, so as to effectively fuse the prior information of these two different properties, and obtain satisfactory results.

[0016] figure 1 It is a schematic diagram of a semi-supervised clustering method that combines pairwise constraints and attribute ranking information in an embodiment of the present invention. Such as figure 1 As shown, the semi-supervised clustering method for the fusion of pai...

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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...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 王金龙吴舜尧
Owner QINGDAO TECHNOLOGICAL UNIVERSITY
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