Document clustering method and device

Abstract

The invention provides a document clustering method and device. The method includes the steps of A, vectorizing each document to allow each vectorized document to correspond to a document coordinate in a multi-dimensional space; B, clustering the documents into two clusters and acquiring geometric center of each cluster in the multi-dimensional space; C, calculating average radius of each cluster, clustering documents corresponding to the document coordinates in the two clusters into a inseparable category if the average radius satisfies a preset condition, and corresponding the two clusters into two separable categories if the average radius does not satisfy the preset condition; D, executing step B and C in each separable category; E, terminating clustering when each document belongs to the inseparable category; wherein the average radius the average value of the distance from all document coordinates to the geometric centers. By the method, document clustering accuracy and intelligence are increased.

Description

technical field [0001] The invention relates to the Internet field, in particular to a method and device for clustering documents. Background technique [0002] In the environment of the rapid increase of Internet information, how to effectively and accurately obtain the required information has become a technical problem that needs to be solved urgently. Among them, how to cluster network documents to obtain multiple document categories is particularly critical. [0003] The methods for clustering documents in the prior art are less intelligent and need to rely on manual participation, that is, manually input clustering values ​​in advance, and determine the clustering of documents into several categories before clustering can begin. For example, manual input will document Clustered into 3 or 4 categories. When the clustering value entered manually is inaccurate, the clustering effect will be greatly affected. Furthermore, when the number of documents is massive, it is im...

AI Technical Summary

Problems solved by technology

[0003] The methods for clustering documents in the prior art are less intelligent and need to rely on manual participation, that is, manually input clustering values ​​in advance, and determine the clustering of documents into several categories before clustering can begin. For example, manual input will document Clustered into 3 or 4 categories

When the clustering value entered manually is inaccurate, the clustering effect will be greatly affected. Furthermore, when the number of documents is massive, a clustering value cannot be manually given, and the clustering operation is difficult to perform

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