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首页> 外文期刊>IEEE transactions on industrial informatics >A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn
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A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn

机译：减轻客户流失风险的大数据聚类算法

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摘要

As market competition intensifies, customer churn management is increasingly becoming an important means of competitive advantage for companies. However, when dealing with big data in the industry, existing churn prediction models cannot work very well. In addition, decision makers are always faced with imprecise operations management. In response to these difficulties, a new clustering algorithm called semantic-driven subtractive clustering method (SDSCM) is proposed. Experimental results indicate that SDSCM has stronger clustering semantic strength than subtractive clustering method (SCM) and fuzzy c-means (FCM). Then, a parallel SDSCM algorithm is implemented through a Hadoop MapReduce framework. In the case study, the proposed parallel SDSCM algorithm enjoys a fast running speed when compared with the other methods. Furthermore, we provide some marketing strategies in accordance with the clustering results and a simplified marketing activity is simulated to ensure profit maximization.
机译：随着市场竞争的加剧，客户流失管理正日益成为公司竞争优势的重要手段。但是，在处理行业中的大数据时，现有的客户流失预测模型无法很好地发挥作用。此外，决策者始终面临着不精确的运营管理。针对这些困难，提出了一种新的聚类算法，称为语义驱动减法聚类方法（SDSCM）。实验结果表明，SDSCM具有比减法聚类方法（SCM）和模糊c均值（FCM）更好的聚类语义强度。然后，通过Hadoop MapReduce框架实现并行SDSCM算法。在案例研究中，与其他方法相比，本文提出的并行SDSCM算法具有较快的运行速度。此外，我们根据聚类结果提​​供了一些营销策略，并模拟了简化的营销活动以确保利润最大化。

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