Identifying product families using data mining techniques in manufacturing paradigm

Chowdhury, Israt J. & Nayak, Richi (2014) Identifying product families using data mining techniques in manufacturing paradigm. In Nayak, Richi, Li, Xue, Liu, Lin, Ong, Kok-Leong, Zhao, Yanchang, & Kennedy, Paul (Eds.) Australasian Data Mining Conference (AusDM), 27-28 November 2014, Brisbane, Australia.

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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 clus-tering. 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 fre-quent 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 fami-lies. When applied on a real-life manufacturing data, the proposed framework outperforms a current baseline that uses orthogonal Procrustes for grouping product families.

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ID Code: 78883
Item Type: Conference Item (Presentation)
Refereed: Yes
Additional URLs:
Keywords: Product families, Bill of Material (BOM), Frequent mining, Matrix representation, Clustering
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MANUFACTURING ENGINEERING (091000)
Australian and New Zealand Standard Research Classification
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Australian Computer Society, Inc.
Copyright Statement: This paper appeared at Australasian Data Mining Conference (AusDM 2014), Brisbane, 27-28 November 2014. Conferences in Research and Practice in Information Technology, Vol. 158. Richi Nayak, Xue Li, Lin Liu, Kok-Leong Ong, Yanchang Zhao, Paul Kennedy Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included.
Deposited On: 23 Nov 2014 23:46
Last Modified: 24 Nov 2014 00:07

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