Big data clustering algorithm for reducing risk of customer losing

Abstract

The invention relates to a big data clustering algorithm for reducing the risk of customer losing. The method comprises the following steps that: (1), related attributes are selected by using an axiomatic fuzzy set theory and a fuzzy concept is expressed by using a membership function and logical operation; (2), according to the calculated membership degree, a neighborhood radius and a weight coefficient of a subtractive clustering algorithm are automatically determined; (3), with the subtractive clustering algorithm, a mountain function is selected and updated to calculate a clustering numberand centroid, wherein the subtractive clustering algorithm and the axiomatic fuzzy set are integrated into a semantic-driven subtractive clustering method; and (4), on the basis of a K-means algorithm, the clustering of the clustering centroid obtained by the semantic-driven subtractive clustering method is calculated. Therefore, with the semantic-driven subtractive clustering method (SDSCM) based on the subtractive clustering algorithm and the axiomatic fuzzy set, the clustering precision of the subtractive clustering algorithm and K-means method is improved; and because of the novel algorithm, the risk of inaccuracy of operational management based on the axiomatic fuzzy sets (AFS) is reduced.

Description

technical field [0001] The invention relates to a clustering algorithm, that is, a semantic subtraction clustering algorithm (SDSCM), in particular to a big data clustering algorithm for reducing the risk of customer loss. Background technique [0002] At present, with the intensification of market competition, customer churn management has become an important means of enterprise's competitive advantage. At present, there are many algorithms for predicting customer churn based on big data, but none of them can predict customer churn well, and decision makers cannot rely on them for precise operation and management. class algorithm. The present invention provides a new method to help companies better reduce the risk of customer churn, thereby obtaining higher profits. Contents of the invention [0003] In order to solve the problems existing in the prior art, the present invention discloses a big data clustering algorithm that reduces the risk of customer loss. The algori...

AI Technical Summary

Problems solved by technology

At present, there are many algorithms for predicting customer churn based on big data, but none of them can predict customer churn well, and decision makers cannot rely on them for precise operation and management. class algorithm

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