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Automatic customer classification method based on K-Means clustering

An automatic classification and customer technology, applied in the field of data processing, can solve problems such as poor objectivity, waste of resources, time-consuming, etc., to achieve the effect of improving customer experience, improving work efficiency, and saving customer time

Pending Publication Date: 2021-06-04
青岛檬豆网络科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] (1) The objectivity is not strong. In the classification process of different people, personal subjective factors will be mixed in it. At the same time, the classification standard for customers will also be limited and affected by human subjective factors, so it will cause the classification results of customers not objective
[0006] (2) Waste of resources. It takes a lot of time to classify a large number of customers manually, which will cause a waste of human resources for the platform.
[0007] However, there is a lack of suitable automatic classification methods in the prior art

Method used

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  • Automatic customer classification method based on K-Means clustering
  • Automatic customer classification method based on K-Means clustering
  • Automatic customer classification method based on K-Means clustering

Examples

Experimental program
Comparison scheme
Effect test

example 2

[0172] Example 2. Calculation example of K-Means clustering algorithm

[0173] Suppose there is data as follows:

[0174] o 1 (0, 2), O 2 (0,0),O 3 (1.5, 0), O 4 (5,0),O 5 (5, 2)

[0175] 1. Choose O 1 (0, 2), O 2 (0, 0) is the initial cluster center, namely M 1 =O 1 =(0,2), M 2 =O 2 =(0,0).

[0176] 2. For each remaining object, assign it to the nearest class according to its distance from each cluster center.

[0177] to O 3 :

[0178]

[0179]

[0180] Since d(M 2 , O 3 )≤d(M 1 , O 3 ), so the O 3 assigned to C 2 .

[0181] to O 4 :

[0182]

[0183]

[0184] Since d(M 2 , O 4 )≤d(M 1 , O 4 ), so the O 4 assigned to C 2 .

[0185] to O 5 :

[0186]

[0187]

[0188] Since d(M 1 , O 5 )≤d(M 2 , O 5 ), so the O 5 assigned to C 1 .

[0189] Update, get new class C 1 ={O 1 , O 5} and C 2 ={O 2 , O 3 , O 4}, the center is M 1 =(0,2), M 2 =(0,0). Compute the squared error criterion, for a single variance:

[0190...

example 3

[0202] Example 3. Calculation example of K value determined by contour coefficient method

example 1

[0203] Example 1: According to the result after clustering in Example 2: K=2, clustering result C 1 ={O 1 , O 5} and C 2 ={O 2 , O 3 , O 4}, where O 1 (0, 2), O 2 (0,0),O 3 (1.5, 0), O 4 (5,0),O 5 (5, 2).

[0204] 1. Calculate the degree of dissimilarity within the sample class (calculate O 1 , O 2 Intra-class dissimilarity as an example):

[0205]

[0206]

[0207] 2. Calculate the dissimilarity between sample classes (calculate O 1 ,O 2 The dissimilarity between classes is taken as an example):

[0208] Since K=2 in this example, that is, the inter-class dissimilarity is the inter-class dissimilarity between the sample and another class.

[0209]

[0210]

[0211] 3. Calculate the silhouette coefficient of the sample (calculate O 1 ,O 2 The dissimilarity between classes is taken as an example):

[0212]

[0213]

[0214] 4. Calculate the overall silhouette coefficient of the cluster when K=2:

[0215]

[0216] Silhouette coefficient p...

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Abstract

The invention provides an automatic customer classification method based on K-Means clustering. The method comprises the steps: carrying out the initialization of platform customer data, forming a data set matrix A through the data of all company customers on a platform, enabling the data dimension to correspond to the column number in the matrix, enabling the customer number to correspond to the line number in the matrix, carrying out the normalization processing of the data set matrix A, and carrying out the classification of the customer data. Obtaining a normalized data set matrix B; a K-means clustering method is adopted to automatically classify clients, a contour coefficient method is adopted to determine a K value, and then an initial clustering center and K clustering centers are determined; all samples in the data set matrix B are distributed to the nearest clustering set according to the principle of minimum distance, and the mean value of all the samples in each cluster serves as a new clustering center; and repeating the above steps until the clustering center does not change any more, and obtaining K clusters, namely, a result of automatically classifying the clients. According to the method, objectivity of classification results can be guaranteed, and labor cost is saved.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to an automatic customer classification method based on K-Means clustering. Background technique [0002] In the field of education, Confucius put forward the educational concept of "teaching students according to their aptitude", and personalized learning is the ideal state pursued by education. For the service industry, personalized service is the ideal state it pursues. [0003] With the rapid development of the Internet, the network provides technical support for the personalized service of the Internet platform with its powerful interactive and distributed characteristics. There is a large amount of customer data in the database of the Internet e-commerce platform. Using these data to classify customers and provide more personalized services to different types of customers can bring more benefits to the platform. [0004] For the classification of existing...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/906G06K9/62G06Q30/02
CPCG06F16/906G06Q30/0201G06F18/23213
Inventor 霍胜军郑鑫于德尚徐楠楠
Owner 青岛檬豆网络科技有限公司
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