Clustering method, device and terminal equipment

Abstract

Embodiments of the present disclosure disclose a clustering method, apparatus, and terminal device. The method first determines that the number of neighbor objects in at least one neighborhood of an object to be accessed in multiple neighborhoods is not less than a corresponding preset threshold, and then determines The object to be accessed is the core object; the objects to be accessed are classified into one category; the objects that are directly accessible to the object to be accessed in the specified neighborhood are then extended and clustered; If it is not a core object, stop the expansion; then, continue to determine the next object to be accessed until all the objects to be accessed are clustered. Since the clustering method uses multiple neighborhood classes to judge whether the object to be accessed is a core object, which is equivalent to loosening the restrictions on the scanning radius and the minimum number of included objects, thus reducing the impact of clustering results on the scanning radius The sensitivity of the two parameters and the minimum number of included objects improves the accuracy of the clustering results.

Description

technical field [0001] The present disclosure relates to the technical field of data processing, and in particular to a clustering method, device and terminal equipment. Background technique [0002] Clustering is the process of dividing a collection of physical or abstract objects into multiple classes composed of similar objects, that is, the process of classifying objects into different classes or clusters. Objects in the same class have great similarity, and different classes There is a great deal of dissimilarity between the objects. [0003] Clustering methods include many types, among which, the density-based clustering method is different from other clustering methods in that it is not based on various distances, but based on density, as long as the density of points in an area is greater than a certain threshold , add it to the clusters that are close to it. This can overcome the shortcoming that the distance-based clustering algorithm can only find "circle-like" ...

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Problems solved by technology

However, the clustering results of this clustering algorithm are very sensitive to the values ​​of the parameters Eps and MinPts, that is, the values ​​of Eps and MinPts are different, resulting in different clustering results, resulting in uncertainty of the clustering results

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