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Customer segmentation method and device based on cluster analysis

A cluster analysis, customer technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve problems such as subdivision, and achieve the effect of avoiding randomness and better clustering effect

Inactive Publication Date: 2018-11-02
QILU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Aiming at the deficiencies in the prior art and solving the problem of how to better segment customers in the prior art, the present invention provides a method and device for customer segmentation based on cluster analysis. In customer segmentation, according to The consumption habits of customers group them into different categories, providing the basis for different marketing strategies for different categories of customers

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  • Customer segmentation method and device based on cluster analysis
  • Customer segmentation method and device based on cluster analysis
  • Customer segmentation method and device based on cluster analysis

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Experimental program
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Effect test

Embodiment 1

[0056] The purpose of Embodiment 1 is to provide a customer segmentation method based on cluster analysis.

[0057] In order to achieve the above object, the present invention adopts the following technical scheme:

[0058] Such as figure 1 as shown,

[0059] A method of customer segmentation based on cluster analysis, the method includes:

[0060] Step (1): Obtain the original data set of customer information, perform numerical preprocessing to obtain data samples, and perform dimensionality reduction and feature extraction on the data samples through an autoencoder;

[0061] Step (2): process the data processed by the autoencoder through the improved k-meams algorithm to obtain the clustering result, and complete the customer segmentation work;

[0062] Step (2-1): The data samples processed by the autoencoder are used to calculate the weight of the attribute features using the variation coefficient method, and the weighted Euclidean distance formula is used to calculate ...

Embodiment 2

[0101] The purpose of Embodiment 2 is to provide a computer-readable storage medium.

[0102] In order to achieve the above object, the present invention adopts the following technical scheme:

[0103] A computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are adapted to be loaded by a processor of a terminal device and perform the following processing:

[0104] Step (1): Obtain the original data set of customer information, perform numerical preprocessing to obtain data samples, and perform dimensionality reduction and feature extraction on the data samples through an autoencoder;

[0105] Step (2): process the data processed by the autoencoder through the improved k-meams algorithm to obtain the clustering result, and complete the customer segmentation work;

[0106] Step (2-1): The data samples processed by the autoencoder are used to calculate the weight of the attribute features using the variation coefficient method, and ...

Embodiment 3

[0109] The purpose of Embodiment 3 is to provide a client segmentation device based on cluster analysis.

[0110] In order to achieve the above object, the present invention adopts the following technical scheme:

[0111] A customer segmentation system device based on cluster analysis, comprising a processor and a computer-readable storage medium, the processor is used to implement instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being executed by the processor Load and perform the following processing:

[0112] Step (1): Obtain the original data set of customer information, perform numerical preprocessing to obtain data samples, and perform dimensionality reduction and feature extraction on the data samples through an autoencoder;

[0113] Step (2): process the data processed by the autoencoder through the improved k-meams algorithm to obtain the clustering result, and complete the customer segme...

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Abstract

The invention discloses a customer segmentation method and device based on cluster analysis. The method comprises the following steps: acquiring a customer information original data set to perform numerical preprocessing so as to obtain a data sample; performing dimension-reduction and feature extraction on the data sample through an automatic encoder, performing the variation coefficient method on the data sample processed by the automatic encoder so as to compute the weight of the attribute feature, and computing the distance between the sample points by adopting the weighted Euclidean distance formula; computing the average distance among all data samples, traversing an adjacent point, wherein the distance between each sample point and the adjacent point is less than the average distance, and counting all sample adjacent point amount and sorting according to descending order to determine k initial cluster center points; and clustering the remaining data according to the weighted Euclidean distance formula to accomplish the customer segmentation method.

Description

technical field [0001] The invention belongs to the technical field of market statistics and marketing, and relates to a customer segmentation method and device based on cluster analysis. Background technique [0002] With the rapid development of science and technology and the popularization and use of computers, the network has quietly penetrated into all aspects of our daily life. Nowadays, it is becoming more and more important for people to use data mining technology to find valuable information from various fields, so that not only can the past development situation be summarized, but also the future development trend of data can be predicted. Among them, customer segmentation is an important research field. Through the method of cluster analysis, and according to the similarity and dissimilarity of customers, they are divided into different categories, which is convenient for enterprises to find different types of customers, so as to formulate differentiated sales pl...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q30/02
CPCG06Q30/0201G06F18/23213
Inventor 王新刚王琳琳孙涛姜雪松耿玉水鲁芹李爱民
Owner QILU UNIV OF TECH
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