Characteristic quantification method of graininess-variable text cluster

Abstract

The invention provides a variable granularity text clustering characteristic quantification method, which is realized by the following steps: firstly, concept expansion of keywords of a file, namely a keyword set in the file is expanded into a concept word set with higher semantic covering capacity by utilization of a knowledge network; secondly, calculation of characteristic representation and similarity, namely the similarity between words can be comprehended as the overlap ratio of common characteristics, and the similarity between files which apply text clustering can also be judged by examining the number of the common characteristics between the files; and thirdly, achievement of the effect of variable granularity clustering through combined use of the variable granularity text clustering characteristic quantification technology and detailed clustering algorithms. The variable granularity text clustering characteristic quantification method overcomes the defect of poor clustering effect under the condition of variable granularity clustering due to inappropriate characteristic quantification of the prior file clustering system.

Description

(1) Technical field [0001] The invention relates to a feature quantization technology of variable granularity text clustering. (2) Background technology [0002] In recent years, a surprising number of text documents have become readily available from a variety of sources. There is thus growing interest in developing technologies that can help users navigate, organize, and summarize this textual information efficiently. High-quality text clustering techniques play an important role in achieving this goal. By organizing a large amount of information into a small number of meaningful clusters, people can observe the data from a macro perspective. This technology provides navigation and browsing mechanisms that greatly improve retrieval performance. [0003] Text clustering for Internet applications has become a technology that is rising and quickly recognized by the market. For example, the core text clustering technology (http: / / vivisimo.com / ) used in VIVISIMO, a clusteri...

AI Technical Summary

Problems solved by technology

[0005] The word frequency VSM modeling method commonly used by many text clustering methods is in the case of large-grained clustering, because it is not sensitive enough to language phenomena, it is difficult to capture the common characteristics between documents, which will lead to more mistakes in the clustering results

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