Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/7093
Title: Clustering for decision support in the fashion industry: A case study
Authors: Monte,A
Soares,C
Pedro Brito
Byvoet,M
Issue Date: 2013
Abstract: The scope of this work is the segmentation of the orders of Bivolino, a Belgian company that sells custom tailored shirts. The segmentation is done based on clustering, following a Data Mining approach. We use the K-Medoids clustering method because it is less sensitive to outliers than other methods and it can handle nominal variables, which are the most common in the data used in this work. We interpret the results from both the design and marketing perspectives. The results of this analysis contain useful knowledge for the company regarding its business. This knowledge, as well as the continued usage of clustering to support both the design and marketing processes, is expected to allow Bivolino to make important business decisions and, thus, obtain competitive advantage over its competitors. © Springer International Publishing Switzerland 2013.
URI: http://repositorio.inesctec.pt/handle/123456789/7093
http://dx.doi.org/10.1007/978-3-319-00557-7_82
metadata.dc.type: bookPart
Publication
Appears in Collections:LIAAD - Book Chapters

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