Tag-co-occurred tag clustering method

Abstract

The invention provides a tag-co-occurred tag clustering method. A tag matrix, a common tag matrix, a tag importance degree matrix and a similarity matrix are defined in order to improve clustering effectiveness; feature vectors of tags are determined by extracting tag co-occurrence information; the method that the distance between one object and another object is calculated by using geometrical distance in the traditional clustering algorithm is changed into the method by using Pearson correlation coefficient in the way that the similarity is calculated by extracting the feature vectors; the tag-co-occurred tag clustering method which is used for clustering the tags by being combined with the K-means clustering algorithm is provided. The clustering method provided by the invention is better than the other clustering methods in effect and has good effectiveness and feasibility.

Description

technical field [0001] The invention relates to the technical field of network label clustering, in particular to a label clustering method for label co-occurrence. Background technique [0002] Tags are the user's subjective understanding of information and an intermediary between objective information and subjective understanding. In social networks, information is linked together through the same label, and users are also linked with other resources and users by using labels, so that people can use labels to communicate, make friends, and other operations. As a part of the online social network, tags have been extensively studied. Websites such as Flickr, del.icio.us, Douban.com, and Youtobe have all adopted collaborative labeling and clustering of tags. There are relatively few studies. At this stage, the research on optimizing the label system mainly focuses on the label cloud and the orderly organization of labels. The research on the correlation degree between tags...

AI Technical Summary

Problems solved by technology

[0011] On the basis of the above research, the present invention is based on improving the previous tag clustering method, and solves the problems of low accuracy of tag description resources, chaotic organization, and ambiguous semantics.

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