Abstract
Mass customization is a new production mode, which is product-family-oriented instead of a single product-oriented, compared with traditional modes. It focuses much more on the group needs of the customer in the phase of product requirements analysis, and cluster analysis becomes a key technology to implement mass customization successfully. K-means as an efficient method and adapted to the large sample of data clustering in the analysis and is widely used. Its number of clusters is determined by way of enumerations, and it is difficult to determine the optimal number of clusters. As the target number of clusters K gradually increases and with combination of research of characteristics of product requirement cluster analysis, a new method for determining the optimal number of clusters based on K-means algorithm is given in this paper.
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Li, Yg., Huang, Ys. (2013). Product Requirements Cluster Analysis Based on K-Means. In: Qi, E., Shen, J., Dou, R. (eds) Proceedings of 20th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40063-6_46
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DOI: https://doi.org/10.1007/978-3-642-40063-6_46
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