Clustering evaluation improvement method and device, computer equipment and storage medium

A technology of clustering and cohesion, applied in computer parts, calculations, instruments, etc., can solve the problems of not considering individual weights, unable to highlight extreme value effects, unable to identify features, etc.

Pending Publication Date: 2021-01-15
上海移卓网络科技有限公司
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Problems solved by technology

Obviously, the current calculation method does not consider the influence of individual weights on the mean, and cannot identify mor

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  • Clustering evaluation improvement method and device, computer equipment and storage medium
  • Clustering evaluation improvement method and device, computer equipment and storage medium
  • Clustering evaluation improvement method and device, computer equipment and storage medium

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Embodiment Construction

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0036] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude one or Presence or addition of multiple other features, integers, steps, operations, elements, components and / or collections thereof.

[0037] It should also be understood that the terminology used ...

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Abstract

The invention discloses a clustering evaluation improvement method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a clustering sample of an advertisement business user, and constructing a clustering sample cluster according to the clustering sample; calculating distance values between the clustering samples and other clustering samples in the clustering sample cluster respectively, and forming distance vectors; normalizing the distance vector to obtain a weight vector and a weighted average value; obtaining a weighted average value of distances between the clustering samples and other clustering samples in the clustering sample cluster as a cohesion degree in the cluster; taking the weighted average value between the clustering samples and all clustering samples in the clustering sample cluster with the minimum distance as the separation degree of the clustering sample cluster with the minimum distance; and taking the difference between the separation degree and the cohesion degree as a molecule, taking the maximum value of the cohesion degree and the separation degree as a denominator, and taking a score value as an evaluation coefficient. The classification of advertisement service users is more reasonable, the user identification is improved, and the average weight effect of arithmetic mean values is improved.

Description

technical field [0001] The invention relates to the technical field of advertisement data processing, in particular to an improved method, device, computer equipment and storage medium for cluster evaluation. Background technique [0002] In the current technology, in the advertising business, it is necessary to classify user groups to facilitate accurate user marketing and operations. The accuracy of classification determines the quality of the effect. The k-means algorithm is commonly used for clustering. However, when k-means clustering is currently performed on advertising business users, the cluster evaluation adopts the silhouette coefficient, which uses the average distance between the advertising business user sample x and other points in the cluster as the cohesion in the cluster Degree a, the average value between the sample x and all points in the nearest cluster is regarded as the degree of separation b from the nearest cluster, and then the difference between th...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q30/02
CPCG06Q30/0251G06F18/23213
Inventor 冯文武郑晓峰
Owner 上海移卓网络科技有限公司
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