Skip to main content

The Construction of Action Rules to Raise Artwork Prices

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12117)


This work explores the development of action rules for changing the prices of works of contemporary fine art. It focuses on the generation of action rules using LISp-Miner related to artwork profiles and artist descriptions. Additionally, this work explores the use of the dominant color of an artwork as a feature in the generation of action rules for adjusting its prices.


  • Art analytics
  • Data mining
  • Action rules

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-59491-6_2
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-59491-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)


  1. The Hiscox Online Art Trade Report 2018. Technical report, ArtTactic (2018).

  2. Beautiful Soup (2019).

  3. Color by name (2019).

  4. Selenium (2019).

  5. (2020).

  6. Aggarwal, C.C.: Machine Learning for Text. Springer, Cham (2018).

    CrossRef  MATH  Google Scholar 

  7. Bailey, J.: Machine Learning for Art Valuation. An Interview With Ahmed Hosny, December 2017.

  8. Berlin, B., Kay, P.: Basic Color Terms: Their Universality and Evolution. University of California Press, Berkeley (1969)

    Google Scholar 

  9. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python, 1st edn. O’Reilly Media, Inc., Sebastopol (2009)

    MATH  Google Scholar 

  10. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–8(6), 679–698 (1986).

    CrossRef  Google Scholar 

  11. Galbraith, J., Hodgson, D.: Econometric fine art valuation by combining hedonic and repeat-sales information. Econometrics 6(3), 32 (2018).

    CrossRef  Google Scholar 

  12. Hajja, A.: Object-driven and temporal action rules mining (2013).

  13. Hosny, A., Huang, J., Wang, Y.: The Green Canvas (2014).

  14. Hutto, C., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014 (2015)

    Google Scholar 

  15. Kang, D., Shim, H., Yoon, K.: A method for extracting emotion using colors comprise the painting image. Multimed. Tools Appl. 77(4), 4985–5002 (2017).

    CrossRef  Google Scholar 

  16. Labrecque, L.I., Milne, G.R.: Exciting red and competent blue: the importance of color in marketing. J. Acad. Mark. Sci. 40(5), 711–727 (2012).

    CrossRef  Google Scholar 

  17. Lindsey, D.T., Brown, A.M.: Universality of color names. Proc. Natl. Acad. Sci. 103(44), 16608–16613 (2006).

    CrossRef  Google Scholar 

  18. Liu, D., Woodham, D.: Using AI to Predict Rothko Paintings’ Auction Prices (2019).

  19. Nekvapil, V.: Using the ac4ft-miner procedure in the medical domain [online] (2009). Accessed 03 Jan 2020

  20. Pawlowski, C., Gelich, A., Raś, Z.W.: Can we build recommender system for artwork evaluation? In: Bembenik, R., Skonieczny, Ł., Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds.) Intelligent Methods and Big Data in Industrial Applications. SBD, vol. 40, pp. 41–52. Springer, Cham (2019).

    CrossRef  Google Scholar 

  21. Powell, L., Gelich, A., Ras, Z.W.: Developing artwork pricing models for online art sales using text analytics. In: Mihálydeák, T., et al. (eds.) IJCRS 2019. LNCS (LNAI), vol. 11499, pp. 480–494. Springer, Cham (2019).

    CrossRef  Google Scholar 

  22. Powell, L., Gelich, A., Ras, Z.W.: Applying analytics to artist provided text to model prices of fine art. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) Complex Pattern Mining. SCI, vol. 880, pp. 189–211. Springer, Cham (2020).

    CrossRef  Google Scholar 

  23. Powell, L., Gelich, A., Ras, Z.W.: Art innovation systems for value tagging. In: Encyclopedia of Organizational Knowledge, Administration, and Technologies. IGI Global (2020).

  24. Ras, Z.W., Wieczorkowska, A.: Action-rules: how to increase profit of a company. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000).

    CrossRef  Google Scholar 

  25. Rauch, J., Šimůnek, M.: Action rules and the GUHA method: preliminary considerations and results. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS (LNAI), vol. 5722, pp. 76–87. Springer, Heidelberg (2009).

    CrossRef  Google Scholar 

  26. Rauch, J., et al.: LISp-Miner, October 2019.

  27. Rawlins, C., Johnson, P.: Selling on eBay: persuasive communication advice based on analysis of auction item descriptions. J. Strateg. E-Commerce 5(1&2), 75–81 (2007)

    Google Scholar 

  28. Roberson, D., Hanley, J.: Color vision: color categories vary with language after all. Curr. Biol. 17(15), R605–R607 (2007).

    CrossRef  Google Scholar 

  29. Saleh, B., Elgammal, A.: Large-scale classification of fine-art paintings: learning the right metric on the right feature. arXiv:1505.00855 [cs], May 2015.

  30. Sharma, G., Wu, W., Dalal, E.N.: The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res. Appl. 30(1), 21–30 (2004).

    CrossRef  Google Scholar 

  31. Taylor, G.: Python-colormath (2014)

    Google Scholar 

  32. Team, O.: OpenCV, October 2017

    Google Scholar 

  33. Tzacheva, A.A., Bagavathi, A., Ayila, L.: Discovery of action rules at lowest cost in spark. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, pp. 87–94. IEEE, November 2017.

  34. Tzacheva, A.A., Raś, Z.W.: Action rules mining. Int. J. Intell. Syst. 20(7), 719–736 (2005).

    CrossRef  MATH  Google Scholar 

  35. Velthuis, O.: Talking Prices: Symbolic Meanings of Prices on the Market for Contemporary Art. Princeton University Press (2005).

Download references


This research is supported by the National Science Foundation under grant IIP 1749105. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Laurel Powell .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Powell, L., Gelich, A., Ras, Z.W. (2020). The Construction of Action Rules to Raise Artwork Prices. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59490-9

  • Online ISBN: 978-3-030-59491-6

  • eBook Packages: Computer ScienceComputer Science (R0)