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Boundary data partitioning method and device

A boundary data and data technology, applied in the computer field, can solve problems such as inability to divide boundary data, and achieve the effect of accurate classification

Active Publication Date: 2017-12-26
ALIBABA GRP HLDG LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] One purpose of this application is to provide a boundary data division method and equipment to solve the problem that the boundary data in the clustering results cannot be accurately divided in the prior art

Method used

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  • Boundary data partitioning method and device

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Embodiment Construction

[0046] The application will be described in further detail below in conjunction with the accompanying drawings.

[0047] In a typical configuration of the present application, the terminal, the device serving the network and the trusted party all include one or more processors (CPU), input / output interface, network interface and memory.

[0048] Memory may include non-permanent storage in computer readable media, in the form of random access memory (RAM) and / or nonvolatile memory such as read only memory (ROM) or flash RAM. Memory is an example of computer readable media.

[0049] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented by any method or technology for storage of information. Information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access ...

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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 ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 王曜
Owner ALIBABA GRP HLDG LTD
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