TY - JOUR
T1 - Cluster Analysis in Data‐Driven Management and Decisions
AU - Sun, Leilei
AU - Chen, Guoqing
AU - Xiong, Hui
AU - Guo, Chonghui
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/12
Y1 - 2017/12
N2 - Clustering 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.
AB - Clustering 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.
KW - Cluster analysis
KW - Clustering, Data-driven
KW - Decision making
KW - Management
UR - https://www.scopus.com/pages/publications/85073252897
U2 - 10.3724/SP.J.1383.204011
DO - 10.3724/SP.J.1383.204011
M3 - 文章
AN - SCOPUS:85073252897
SN - 2096-2320
VL - 2
SP - 227
EP - 251
JO - Journal of Management Science and Engineering
JF - Journal of Management Science and Engineering
IS - 4
ER -