One of the critical issues that today’s online firms face is to make sense of all the available data about their customers and to offer them customised and personalised services with affective features. There are numerous clustering methodologies that can help companies identify homogeneous groups of people among their potential customers so that they can design such services for each homogenous group. Because firms do not have prior external knowledge about the true clusters of their potential customers, deciding which clustering method to use becomes extremely challenging. This paper compared two most popular algorithms including k-means and fuzzy c-means clustering methodologies. The results showed that compared to fuzzy c-means clustering k-means clustering yielded an imprecise categorisation of as much as 72% of the potential shoppers of an online shopping service. Moreover, the results showed that compared to k-means clustering, fuzzy c-means clustering led to better cluster solutions based on multiple criteria. The paper shows how the results can help online businesses design their online offerings with effective features.

中文翻译：

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在网上购物的背景下使用大数据进行情感设计

当今在线公司面临的关键问题之一是了解有关其客户的所有可用数据，并为他们提供具有情感特征的定制和个性化服务。有许多聚类方法可以帮助公司识别潜在客户中的同质人群，以便他们可以为每个同质人群设计此类服务。由于公司事先没有关于潜在客户的真实集群的外部知识，因此决定使用哪种集群方法变得极具挑战性。本文比较了两种最流行的算法，包括 k-means 和模糊 c-means 聚类方法。结果表明，与模糊 c 均值聚类相比，k 均值聚类对在线购物服务的潜在购物者的分类不精确，高达 72%。此外，结果表明，与 k-means 聚类相比，模糊 c-means 聚类导致基于多个标准的更好的聚类解决方案。该论文展示了结果如何帮助在线企业设计具有有效功能的在线产品。