大数据背景下基于聚类算法的客户细分方法探讨

Wenbo Zhao
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引用次数: 0

摘要

伴随着大数据技术的不断发展,各行各业都深知大数据的优势,将其广泛应用于客户服务工作中,尤其是在客户细分工作的支持方面,取得了良好的效果。本文针对传统数据挖掘过程中存在的聚类结果波动大、聚类纯度低等问题,提出了改进聚类算法的大数据精准挖掘技术。并将其应用于客户细分领域,实验结果表明,改进聚类算法应用于客户细分,结果曲线波动幅度小,聚类纯度明显高于传统算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Exploration of Customer Segmentation Methods Based on Clustering Algorithm in the Context of Big Data
Accompanied by the continuous development of big data technology, various industries are well aware of the advantages of big data, which are widely used in customer service work, especially in the support of customer segmentation work, and have achieved good results. In this paper, for the problems of large fluctuation of clustering results and low clustering purity in the traditional data mining process, the big data precision mining technology with improved clustering algorithm is proposed. And it is applied in the field of customer segmentation, and the experimental results show that the improved clustering algorithm is applied in customer segmentation, the result curve fluctuation amplitude is small, and the clustering purity is significantly higher than the traditional algorithm.
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