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Supervised Regression Clustering: A Case Study for Fashion Products
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Author(s): Ali Fallah Tehrani (Technology Campus Grafenau, Deggendorf Institute of Technology, Grafenau, Germany)and Diane Ahrens (Technology Campus Grafenau, Deggendorf Institute of Technology, Grafenau, Germany)
Copyright: 2016
Volume: 3
Issue: 4
Pages: 20
Source title:
International Journal of Business Analytics (IJBAN)
Editor(s)-in-Chief: John Wang (Montclair State University, USA)
DOI: 10.4018/IJBAN.2016100102
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Abstract
Clustering techniques typically group similar instances underlying individual attributes by supposing that similar instances have similar attributes characteristic. On contrary, clustering similar instances given a specific behavior is framed through supervised learning. For instance, which fashion products have similar behavior in term of sales. Unfortunately, conventional clustering methods cannot tackle this case, since they handle attributes by a same manner. In fact, conventional clustering approaches do not consider any response, and moreover they assume attributes act by the same importance. However, clustering instances with respect to responses leads to a better data analytics. In this research, the authors introduce an approach for the goal supervised clustering and show its advantage in terms of data analytics as well as prediction. To verify the feasibility and the performance of this approach the authors conducted several experiments on a real dataset derived from an apparel industry.
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