Method and device for hierarchical clustering

Abstract

The invention relates to a method and a device for hierarchical clustering, and belongs to the field of data mining. The method comprises the steps of obtaining a data object set to be clustered, wherein the data object set comprises a plurality of classes, and each class corresponds to at least one data object; clustering the data objects corresponding to the first class to obtain a clustering result, wherein the number of the data objects corresponding to the first class is greater than a first preset threshold value, the clustering result comprises a plurality of clusters, and each cluster comprises at least one data object; screening the data objects corresponding to the first class according to the clustering result to obtain representative data objects of the first class; calculating a between-class distance according to the representative data objects of the first class and data objects corresponding to the second class; performing hierarchical clustering on the data object set based on the between-class distance. According to the method and the device for hierarchical clustering, by clustering of the data objects corresponding to the first class, the calculation intensity is reduced, and the calculation time and resources are saved; furthermore, the clustering result is more reliable, and subsequent data analysis is facilitated.

Description

technical field [0001] The present disclosure relates to the field of data mining, in particular to a method and device for hierarchical clustering. Background technique [0002] In the field of data mining, it is usually necessary to analyze a large amount of data in order to obtain valuable analysis results. Clustering algorithm is an important algorithm for analyzing data in the field of data mining. This algorithm is used to classify a set of multiple data according to different categories of data. aggregated into one category to facilitate subsequent data analysis. Among them, hierarchical clustering is a commonly used clustering algorithm. [0003] When the related technology implements the method of hierarchical clustering, it calculates the distance between two classes, that is, the inter-class distance, so as to merge two classes whose inter-class distance is less than a certain value into a new class. Since each class may contain more than one data object, when ...

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Problems solved by technology

[0005] In related technologies, when implementing hierarchical clustering by calculating the distance between classes, the amount of calculation is too large. When there are many data objects contained in a class, it will consume too much time and resources. Moreover, because each class may contain Data objects that do not belong to this class, that is, noise. After using this data object to calculate the inter-class distance and form a new class, more noise may be introduced, resulting in poor clustering results, which is not conducive to subsequent data analysis

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