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Image Clustering for Marketing Purposes

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Challenges at the Interface of Data Analysis, Computer Science, and Optimization

Abstract

Clustering algorithms are standard tools for marketing purposes. For example, in market segmentation, they are applied to derive homogeneous customer groups. However, recently, the available resources for this purpose have extended. So, e.g., in social networks potential customers provide images – and other information as e.g. profiles, contact lists, music or videos – which reflect their activities, interests, and opinions. Also, consumers are getting more and more accustomed to select or upload personal images during an online dialogue. In this paper we discuss, how the application of clustering algorithms to such uploaded image collections can be used for deriving market segments. Software prototypes are discussed and applied.

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References

  • ARD/ZDF (2010) ARD/ZDF-Onlinestudie 2010. www.ard-zdf-onlinestudie.de

  • Baier D (2003) Classification and marketing research. Taksonomia 10:21–39

    Google Scholar 

  • Baier D, Brusch M (2008) Marktsegmentierung. In: Herrmann A, Homburg C, Klarmann M (eds) Handbuch Marktforschung, Methoden - Anwendungen - Praxisbeispiele. Gabler, Wiesbaden, pp 769–790

    Google Scholar 

  • Chen Y, Wang JZ, Krovetz R (2003) Content-based image retrieval by clustering. In: Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR 2003), Berkeley, CA, USA, pp 193–200

    Google Scholar 

  • Choras RS (2007) Image feature extraction techniques and their applications for CBIR and biometrics systems. Int J Biol Biomed Eng 1(1):6–16

    Google Scholar 

  • Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: Ideas, influences, and trends of the new age. ACM Comput Surv 40(2):5:1–5:60

    Google Scholar 

  • Gaul W, Baier D (1994) Marktforschung und Marketing Management: Computerbasierte Entscheidungsunterstützung. Oldenbourg, München

    Google Scholar 

  • van House N (2007) Flickr and public image-sharing: Distant closeness and photo exhibition. In: Conference on Human Factors in Computing Systems, San Jose, CA, USA, pp 2717–2722

    Google Scholar 

  • Law M, Figueiredo M, Jain AK (2004) Simultaneous feature selection and clustering using mixture model. IEEE Trans Pattern Anal Mach Intell 26(9):1154–1166

    Article  Google Scholar 

  • Lew MS, Sebe N, Djeraba C, Jainl R (2006) Content-based multimedia information retrieval: State of the art and challenges. ACM Trans Multimedia Comput Comm Appl 2(1):1–19

    Article  Google Scholar 

  • Maheshwari M, Silakari S, Motwani M (2009) Image clustering using color and texture. In: First International Conference on Computational Intelligence, Communication Systems and Networks, July 23–25, 2009, Indore, India, pp 403–408

    Google Scholar 

  • Rodden K, Basalej W, Sinclair D, Wood KR (2001) Does organisation by similarity assist image browsing? In: Proceedings of Human Factors in Computing Systems, March-31-April 5, 2001, Seattle, Washington, USA, pp 190–197

    Google Scholar 

  • Schmitt I (2005) Ähnlichkeitssuche in Multimedia-Datenbanken - Retrieval, Suchalgorithmen und Anfragebehandlung. Oldenbourg, München

    Google Scholar 

  • Sinus Sociovision (2009) Informationen zu den Sinus-Milieus 2009. Sinus Sociovision GmbH, Heidelberg.

    Google Scholar 

  • Smith W (1956) Product differentiation and market segmentation as alternative marketing strategies. J Market 21:3–8

    Article  Google Scholar 

  • Wedel M, Kamakura WA (2000) Market segmentation: conceptual and methodological foundations. Kluwer, Dordrecht

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, 101344, 03013, Cottbus, Germany

    Daniel Baier & Ines Daniel

Authors
  1. Daniel Baier
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  2. Ines Daniel
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Corresponding author

Correspondence to Daniel Baier .

Editor information

Editors and Affiliations

  1. Fak. Wirtschaftswissenschaften, Inst. Entscheidungstheorieund, Universität Karlsruhe (TH), Kaiserstr. 12, Karlsruhe, 76128, Germany

    Wolfgang A. Gaul

  2. Insitute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Baden-Württemberg, Germany

    Andreas Geyer-Schulz

  3. , Information Systems, University ofHildesheim, Marienburger Platz 22, Hildesheim, 31141, Germany

    Lars Schmidt-Thieme

  4. Institute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, Karlsruhe, 76128, Germany

    Jonas Kunze

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Baier, D., Daniel, I. (2012). Image Clustering for Marketing Purposes. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_49

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