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RETRACTED ARTICLE: Application and research of clustering fusion algorithm in communication network prediction

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This article was retracted on 05 December 2022

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Abstract

In order to solve the problem of clustering fusion algorithm, such as key parameter setting, fusion of “soft” hard clusters, design and selection of consensus functions, we optimize the K-means algorithm. However, this method has many problems in practical application. It requires professionals to specify the number of clusters and make empirical judgments on the results. The improved algorithm of clustering fusion is introduced into the customer segmentation. Based on the data mining of the mobile phone business of a telecom company in a certain city, customer segmentation is carried out, according to the characteristics of customer calls, SMS and other attributes. The results show that the improved clustering fusion algorithm can effectively solve the above problems and get a reasonable clustering result. At the same time, by analyzing the CO association matrix, we can obtain each customer’s belonging class. The purpose of dividing the results is achieved, which makes the data mining more intelligent.

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Authors and Affiliations

  1. NingBo Dahongying University, Ningbo, China

    Xiaolei Li

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  1. Xiaolei Li
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Correspondence to Xiaolei Li.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03877-9

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Li, X. RETRACTED ARTICLE: Application and research of clustering fusion algorithm in communication network prediction. Cluster Comput 22 (Suppl 4), 8429–8436 (2019). https://doi.org/10.1007/s10586-018-1865-0

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  • DOI: https://doi.org/10.1007/s10586-018-1865-0

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