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Automatic Classification for Grouping Designs in Fashion Design Recommendation Agent System

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4251))

Abstract

Nowadays, users spend much time and effort in finding the best suitable designs since more and more information is placed on-line. To save their time and effort in searching the designs they want, the user-adapting recommendation system is required. In this paper, Automatic Classification for Grouping designs (ACG) in a Fashion Design Recommendation Agent System (FDRAS) is proposed. The ACG algorithm groups designs into clusters based on these classified designs. It is possible that if the design requires simultaneous regrouping in all other groups, the ACG algorithm can be used to improve efficiency of information retrieval and sorting, in the FDRAS datasets. The proposed method is evaluated on a large database, significantly outperforming the nearest-neighbor model and k-mean clustering in the prototype user-adapting FDRAS. This method can solve the large-scale dataset problem without deteriorating accuracy quality.

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Author information

Authors and Affiliations

  1. School of Computer Information Engineering, Sangji University, Korea

    Kyung-Yong Jung

Authors
  1. Kyung-Yong Jung
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Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2006 Springer-Verlag Berlin Heidelberg

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Jung, KY. (2006). Automatic Classification for Grouping Designs in Fashion Design Recommendation Agent System. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_38

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  • DOI: https://doi.org/10.1007/11892960_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46535-5

  • Online ISBN: 978-3-540-46536-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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