Clustering evaluation measurement method, system and device and storage medium

Abstract

The invention discloses a clustering evaluation measurement method, system and device and a storage medium, and the method comprises the steps: obtaining a to-be-clustered data set, and processing thedata set through a preset function, so as to generate a sample set with the same dimension and order of magnitude; in combination with a preset clustering algorithm and a set cluster number, generating a plurality of clusters from the sample set, and obtaining an inter-cluster dispersity value and an intra-cluster compactness value; constructing a penalty term according to the logarithmic function, and outputting a first clustering result in combination with the inter-cluster dispersity value, the intra-cluster compactness value and the penalty term. The method generates a sample set througha preset function processing set to-be-clustered data set, generatesg a plurality of clusters from the sample set according to a preset clustering algorithm and a set cluster number, outputs an inter-cluster dispersity value and an intra-cluster compactness value, and finally outputs a first clustering result in combination with the inter-cluster dispersity value, the intra-cluster compactness value and a penalty term. Therefore, the influence of dimensions and outliers of each dimension of the sample on the clustering result is reduced, and the effectiveness of the clustering effect is improved.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a cluster evaluation measurement method, system, device and storage medium. Background technique [0002] With the development of society and the advent of the intelligent age, clustering technology, as an important part of the learning process of unsupervised pattern recognition, has been widely used in important fields such as machine learning, pattern recognition and data mining. The purpose of clustering is to divide the originally scattered and seemingly irrelevant data samples into similar groups or clusters to obtain some internal data rules. A key task of clustering is to quantitatively evaluate the clustering results, especially to determine an optimal number of clusters or partition structure. The quality of the clustering results is determined by the clustering validity. The CH (Calinski-Harabasz) index is a common measurement method used to evaluate t...

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Problems solved by technology

[0003] However, the clustering effectiveness index does not consider the dimension of each dimension of the sample and the influence of outliers when calculating the inter-cluster dispersion of data samples and the intra-cluster compactness of samples, while the dimension of each dimension of the sample and the influence of outliers points will have an important impact on the effectiveness of the clustering effect

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