Identifying product families has been considered as an
effective way to accommodate the increasing product
varieties across the diverse market niches. In this paper,
we propose a novel framework to identifying product
families by using a similarity measure for a common
product design data BOM (Bill of Materials) based on
data mining techniques such as frequent mining and
clustering. For calculating the similarity between BOMs,
a novel Extended Augmented Adjacency Matrix (EAAM)
representation is introduced that consists of information
not only of the content and topology but also of the
frequent structural dependency among the various parts of
a product design. These EAAM representations of BOMs
are compared to calculate the similarity between products
and used as a clustering input to group the product
families. When applied on a real-life manufacturing data,
the proposed framework outperforms a current baseline
that uses orthogonal Procrustes for grouping product
families. . |
| Cite as: Chowdhury, I.J. and Nayak, R. (2014). Identifying Product Families Using Data Mining Techniques in Manufacturing Paradigm. In Proc. Twelfth Australasian Data Mining Conference (AusDM14) Brisbane, Australia. CRPIT, 158. Li, X., Liu, L., Ong, K.L. and Zhao, Y. Eds., ACS. 113-120 |
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