Boundary data partitioning method and device

Abstract

An aim of the application is to provide a boundary data partitioning method and device. Specifically, a correlation high-density zone of boundary data is obtained via undisputed data of a correlation clustering group in a clustering result; centralized data in the correlation high-density zone is cut out from the undisputed data of the correlation clustering group, similarity between the boundary data and the centralized data in the correlation high-density zone is analyzed, the boundary data is partitioned based on the similarity, and the boundary data can be accurately classified in a lossless manner.

Description

technical field

[0001] The present application relates to the field of computers, and in particular to a technology for boundary data division. Background technique

[0002] At present, the K-Means clustering algorithm is often used in clustering-related data mining work to cluster and divide the data. The K-Means clustering algorithm mainly divides the data into multiple different points based on the distance between two points. clustering group. In the K-Means clustering algorithm, the closer the distance between a certain data and the center point of the cluster group, the higher the similarity between the data and other data in the cluster group. In other words, the K-Means clustering algorithm divides the data into the cluster group to which the center point closest to the data belongs. However, in actual scenarios, when a piece of data is consistent with the distance between the center points of two or more clustering groups, the data will not be classified into the ...

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