Weighted K-means method for grouping of power customer values

Abstract

The invention discloses a weighted K-means method for grouping of power customer values. The invention uses a weighted K-means clustering algorithm which is suitable for the characteristic variable data features of power customers. The algorithm comprises steps of first determining that the sum of standard deviations of the data in a weighted power customer group is used as a clustering criterion function, wherein the weight is a ratio of the number of customers in the power customer group to a total number of customers; then when calculating the similarity between the power customer objects and a customer group center point according to the criterion function, by using Euclidean distance as a premise, using a data standard deviation in the customer group as a weight of a reference factor to achieve more accurate value grouping of nonuniform-density power customer objects . A grouping clustering result of the weighted K-means clustering algorithm applied to the grouping of power customer values shows that the method is suitable for practical operation data and has an effect of improving clustering compactness. More high-quality grouping clustering results can also ensure efficient decision implementation, and ultimately bring higher profits to the power supply enterprises.

Description

technical field [0001] The invention relates to a clustering method for electric power customer value grouping, in particular to a weighted K-means method for electric power customer value grouping. Background technique [0002] The 21st century is an information age, and information has played a vital role in the influence of all walks of life. Faced with the huge enterprise operation and management data that power supply companies are generating and updating every day, how to use these data to dig out potential customer value from the messy data, and then help power companies improve marketing decisions and reduce operating costs. It is the direction that every power supply company is striving to reduce costs and increase corporate profits. As a data processing method that can find potential information in a large amount of data, data mining technology stands out here. The application of technology will also provide a broader development space for power supply enterprise...

AI Technical Summary

Problems solved by technology

[0004] For the characteristics of large differences in the distribution density of power customer characteristic variable data, if the traditional K-means clustering algorithm is directly used, it will obviously cause the phenomenon that high-density small groups carve up low-density large groups

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