期刊名称：Journal of Management Science and Engineering
印刷版ISSN：2096-2320
出版年度：2017
卷号：2
期号：4
页码：227-251
DOI：10.3724/SP.J.1383.204011
语种：English
出版社：Elsevier
摘要：AbstractClustering plays an important role in management and decision‐making processes. This paper first discusses three types of cluster analysis methods—centroid‐based, connectivity‐based, and density‐based. Then the challenges to traditional clustering in new business environments are highlighted, with algorithmic extensions and innovative efforts for coping with data that is dynamic, large‐scale, representative, non‐convex, and consensus in nature. In addition, three application cases are illustrated, where clustering is incorporated into the overall solution in the contexts of management support, business of sharing economy, and healthcare decision assistance.
关键词：Cluster analysis;Clustering, Data-driven;Management;Decision making
