Big data clustering algorithm for reducing risk of customer losing

A clustering algorithm and customer churn technology, applied in the field of clustering algorithms, can solve the problems of lack of big data clustering algorithm, inability to predict customer churn, etc., to achieve the effect of improving clustering accuracy and reducing the risk of inaccuracy

Inactive Publication Date: 2018-08-17
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

At present, there are many algorithms for predicting customer churn based on big data, but none of them can predict

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  • Big data clustering algorithm for reducing risk of customer losing
  • Big data clustering algorithm for reducing risk of customer losing
  • Big data clustering algorithm for reducing risk of customer losing

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Embodiment Construction

[0037] The specific implementation of the big data clustering algorithm for reducing the risk of customer loss disclosed by the present invention will be described in detail below in conjunction with the accompanying drawings, which is not intended to limit the scope of the present invention.

[0038] The present invention relates to the following theory:

[0039] (1) Axiomatic Fuzzy Sets (AFS). The AFS theory is a new semantic method for dealing with fuzzy information. Its essence is to study how to transform the internal laws or patterns contained in the training data or database into fuzzy sets and their logical operations. Member functions and their logical operations are determined by raw data and facts rather than intuition, imitating the mechanism by which human beings perceive and observe things to form concepts and generate logic, and discuss fuzzy concepts and their logical operations from a more abstract and general level. AFS theory mainly includes two parts: AFS ...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23
Inventor 李果
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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