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Density-based text clustering algorithm

A text clustering algorithm and density technology, applied in the field of keyword extraction and semantic analysis, can solve the problems of inaccurate calculation of similarity between documents, high-dimensional sparseness, and impact on clustering accuracy

Inactive Publication Date: 2017-07-07
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

This text representation method assumes that words are independent, and does not analyze the content of documents semantically, so the similarity between documents cannot be accurately calculated, which affects the accuracy of clustering, but causes the problem of high-dimensional sparseness

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Embodiment Construction

[0041] refer to figure 1 , a density-based text clustering method proposed by the present invention includes the following steps:

[0042] Step A, according to the data set, the text is segmented and the stop words are removed;

[0043] Step B, according to the obtained text word segmentation, according to the three kinds of parts of speech and word frequency of noun, verb, adjective, the corresponding keywords are extracted to the word segmentation;

[0044] Step C, calculating the keyword similarity of the text by using the improved HowNet vocabulary similarity algorithm according to the obtained keywords;

[0045]Step D, calculate the similarity of the text according to the obtained text keyword similarity;

[0046] Step E, according to the obtained text similarity, the text is clustered by a density-based clustering algorithm;

[0047] In the step A, the NLPIR Chinese lexical analysis system of the Institute of Computing Technology of the Chinese Academy of Sciences, na...

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Abstract

The present invention discloses a density-based text clustering algorithm research method. The method comprises the following steps: using the ICTCLAS word segmentation system to carry out word segmentation on a text in a text set, and extracting corresponding keywords from the word segmentation according to the three parts of speech of the noun, the verb, and the adjective, and the word frequency; using an improved HowNet word similarity algorithm to calculate keyword similarity of the obtained keywords; according to the keyword similarity in the text, calculating text similarity; and according to the obtained text similarity, using the density-based clustering algorithm to carry out clustering on the text, so that the performance of the existing text-related information retrieval technology can be significantly improved.

Description

technical field [0001] The invention relates to the field of computer text information processing, in particular to a method for keyword extraction and semantic analysis. Background technique [0002] In recent years, with the large-scale popularization of the network and the improvement of enterprise informatization, various resources have exploded. However, most of the information is stored in text databases. For such semi-structured or unstructured data, However, the means to obtain specific content information are weak, resulting in difficulty in information search and low utilization of information. As a result, researches on text mining, information filtering and information retrieval have reached an unprecedented climax. Fast and high-quality text clustering technology can form a large amount of text information into a few meaningful clusters, and make the text information in the same cluster have a high degree of similarity, while the text between different clusters...

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/35G06F40/289G06F40/30
Inventor 周应华李春婷
Owner CHONGQING UNIV OF POSTS & TELECOMM
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