Full-coverage granular computing based K-medoids text clustering method

Abstract

The invention discloses a full-coverage granular computing based K-medoids text clustering method. The method comprises the steps of 1) preprocessing texts, including, Chinese word segmentation and stop word removal; 2) performing characteristic extraction on the texts, setting a high frequency word threshold and a low frequency word threshold, filtering away high-frequency words with insufficientdiscrimination degrees and low-frequency words with weak representativeness, and then building a word vector spatial model by utilizing a TF-IDF algorithm; and 3) clustering the texts, firstly performing coarse clustering on the texts by utilizing single-pass and calculating an initial clustering center candidate set by utilizing a concept of granularity importance of a full-coverage granular computing theory, and then calculating an initial clustering center based on the density and a maximum-minimum distance algorithm, and finally performing text clustering by utilizing a k-medoids algorithm. The full-coverage granular computing based K-medoids text clustering method solves the problems of iteration times increase and relatively big fluctuation of clustering results of the traditional K-medoids clustering algorithm in which the initial clustering center is selected randomly, and also solves the problem that the initial clustering center is located at the same type of the cluster inthe currently improved K-medoids clustering algorithm.

Description

technical field [0001] The invention relates to full-coverage granule calculation and text mining technology, in particular to the granulation of full-coverage granule calculation and the method of text clustering. Background technique [0002] Problems such as information overload and lack of structure brought about by the rapid development of the Internet make it difficult for people to quickly and accurately obtain interesting and potentially useful content from the massive amount of information, and it is impossible to process this information manually . At present, the vast majority of network information is in the form of text. As unstructured data, text data is not as easy to process as structured data, so the utilization rate of text data is greatly reduced, and most traditional information retrieval technologies Cannot handle massive text data. Data mining is an effective technology to mine hidden information from a large amount of valid data. Text mining is the p...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a kind of K-medoids text clustering method based on full-coverage granule calculation in order to solve the problem of traditional clustering method randomly selecting cluster centers and text clustering method with low accuracy, the method comprising the following steps:

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