Clustering method and device

Abstract

The invention discloses a clustering method and device. Wherein, the method includes: acquiring a neural network model; inputting target data into the neural network model, and determining the activation degree of the target data to at least one intermediate layer node in the neural network model; Select the clustering node of the target data among the nodes; determine the clustering result of the target data according to the clustering node. The invention solves the technical problem that the clustering method combined with the neural network model in the related art has high complexity.

Description

technical field [0001] The present invention relates to the field of clustering, in particular to a clustering method and device. Background technique [0002] Existing data clustering methods, such as K-means clustering (K-means) method and self-organizing map (SelfOrganizing Maps) clustering method, require the user to specify the similarity or similarity calculation method between data samples. The above clustering methods require users to know how to select or design similarity or similarity calculation methods, which increases the difficulty of using data clustering methods. Further, when the similarity or similarity calculation method selected or designed by the user is not appropriate , the clustering effect that the clustering method can achieve may not meet the needs of users. For example, when performing cluster analysis on image data, if the Euclidean distance is used to calculate the similarity between images, it cannot reflect the translation, scale and other c...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present invention provides a clustering method and device to at least solve the technical problem of high complexity in the use of the clustering method combined with the neural network model in the related art

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