Density-based text clustering method, device and equipment, and storage medium

Abstract

The embodiment of the invention discloses a text clustering method, device and equipment based on density and a storage medium, and relates to the technical field of text data analysis. The method comprises the steps of receiving a target data set; determining a target distance formula; generating a distance matrix about the whole target data set; calculating the local density of each data point;separately extracting the minimum value of the distance between each data point and each data point in the sample point set, and recording the minimum value as the minimum point distance; establishinga clustering decision diagram according to the local density and the minimum point distance; determining the number of class clusters and a class cluster center in the clustering decision diagram; and dividing each data point into class clusters of the clustering decision diagram. According to the method, in the whole clustering process, the non-spherical data can be clustered only by calculatingthe distance between the sample points once without iterative calculation, the algorithm performance is greatly improved, the clustering decision diagram is used for scientifically selecting the number of the class clusters, and the situation that the number of the class clusters is manually set without basis is avoided.

Description

technical field [0001] The present application relates to the technical field of text data analysis, in particular to a density-based text clustering method, device, equipment and storage medium. Background technique [0002] Clustering is a typical unsupervised learning method, which divides the samples in the data set into several usually disjoint subsets (clusters / clusters) by learning unlabeled training samples. The goal of cluster analysis is to classify based on the similarity of elements, which is widely used in the fields of bioinformatics and pattern recognition. Commonly used clustering algorithms include: K-means, K-medoids, DBSCAN, etc. [0003] Text clustering is the specific application of clustering algorithms in the field of natural language processing. The usual method is to create text feature vectors based on tfidf (term frequency–inverse document frequency, word frequency-inverse text frequency index), word2vec, etc., and then use various The clustering ...

AI Technical Summary

Problems solved by technology

However, the current various text clustering methods mainly have defects such as the need for iterative calculation, slow convergence, and poor clustering effect on non-spherical data.

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