Mining Statistical Association Rules to Select the Most Relevant Medical Image Features

  • Marcela X. Ribeiro
  • Andre G. R. Balan
  • Joaquim C. Felipe
  • Agma J. M. Traina
  • Caetano TrainaJr.
Part of the Studies in Computational Intelligence book series (SCI, volume 165)


In this chapter we discuss how to take advantage of association rule mining to promote feature selection from low-level image features. Feature selection can significantly improve the precision of content-based queries in image databases by removing noisy and redundant features. A new algorithm named StARMiner is presented. StARMiner aims at finding association rules relating low-level image features to high-level knowledge about the images. Such rules are employed to select the most relevant features. We present a case study in order to highlight how the proposed algorithm performs in different situations, regarding its ability to select the most relevant features that properly distinguish the images. We compare the StARMiner algorithm with other well-known feature selection algorithms, showing that StARMiner reaches higher precision rates. The results obtained corroborate the assumption that association rule mining can effectively support dimensionality reduction in image databases.


Feature Vector Feature Selection Association Rule Relevance Feedback Association Rule Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Intl. Conf. on Management of Data, Washington, D.C, pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Intl. Conf. on Very Large Databases (VLDB), Santiago, Chile, pp. 487–499 (1994)Google Scholar
  3. 3.
    Apte, C., Liu, B., Pednault, E.P.D., Smyth, P.: Business applications of data mining. Communications of the ACM (CACM) 45(8), 49–53 (2002)CrossRefGoogle Scholar
  4. 4.
    Aumann, Y., Lindell, Y.: A statistical theory for quantitative association rules. In: The fifth ACM SIGKDD Intl. Conf. on Knowledge discovery and data mining, San Diego, California, United States, pp. 261–270 (1999)Google Scholar
  5. 5.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. Addison-Wesley, WokinghamGoogle Scholar
  6. 6.
    Balan, A.G.R., Traina, A.J.M., Traina Jr., C.,, P.M.: d. A. Marques. Fractal analysis of image textures for indexing and retrieval by content. In: 18th IEEE Intl. Symposium on Computer-Based Medical Systems - CBMS, Dublin, Ireland, pp. 581–586 (2005)Google Scholar
  7. 7.
    Beyer, K., Godstein, J., Ramakrishnan, R., Shaft, U.: When is ”nearest neighbor” meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  8. 8.
    Cardie, C.: Using decision trees to improve case-based learning. In: 10th Intl. Conf. on Machine Learning, pp. 25–32 (1993)Google Scholar
  9. 9.
    Comer, M.L., Delp, E.J.: The em/mpm algorithm for segmentation of textured images: Analysis and further experimental results. IEEE Trans. on Image Processing 9(10), 1731–1744 (2000)zbMATHCrossRefGoogle Scholar
  10. 10.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Int’l Conf. on Management of Data, Dallas, Texas, USA (2000)Google Scholar
  11. 11.
    Hsu, W., Lee, M.L., Zhang, J.: Image mining: Trends and developments. Journal of Intelligent Information Systems 19(1), 7–23 (2002)CrossRefGoogle Scholar
  12. 12.
    Huang, S.H.: Dimensionality reduction in automatic knowledge acquisition: A simple greedy search approach. IEEE Trans. on Knowledge and Data Engineering (TKDE) 15(6), 1364–1373 (2003)CrossRefGoogle Scholar
  13. 13.
    Kinoshita, S.K., de Azevedo-Marques, P.M., Pereira Jr., R.R., Heisinger Rodrigues, J.A.: Content-based retrieval of mammograms using visual features related to breast density patterns. Journal of Digital Imaging 20(2), 172–190 (2007)CrossRefGoogle Scholar
  14. 14.
    Kira, K., Rendell, L.A.: A practical approach for feature selection. In: 9th Intl. Conf. on Machine Learning, Aberdeen, Scotland, pp. 249–256 (1992)Google Scholar
  15. 15.
    Kononenko, I.: Estimating attributes: Analysis and extension of relief. In: European Conf. on Machine Learning, Catania, Italy, pp. 171–182 (1994)Google Scholar
  16. 16.
    Liu, Y., Zhang, D., Lu, G., Ma, W.-Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recognition Letters 40, 262–282 (2007)zbMATHGoogle Scholar
  17. 17.
    Malcok, M., Aslandogan, Y., Yesildirek, A.: Fractal dimension and similarity search in high-dimensional spatial databases. In: IEEE Intl. Conf. on Information Reuse and Integration, Waikoloa, Hawaii, USA. pp. 380–384 (2006)Google Scholar
  18. 18.
    Molina, L.C., Belanche, L., Nebot, A.: Feature selection algorithms: A survey and experimental evaluation. In: IEEE Intl. Conf. on Data Mining 2002 (ICDM 2002), Washington, DC, USA, pp. 306–404 (2002)Google Scholar
  19. 19.
    Narendra, P.M., Fukunaga, K.: A branch and bound algorithm for feature subset selection. IEEE Trans. On Computer 26(9), 917–922 (1977)zbMATHCrossRefGoogle Scholar
  20. 20.
    Ordonez, C., Ezquerra, N., Santana, C.A.: Constraining and summarizing association rules in medical data. Knowledge and Information Systems 9(3), 259–283 (2006)CrossRefGoogle Scholar
  21. 21.
    Quinlan, R.: C4.5: Programs for Machine Learning, San Mateo, CA (1993)Google Scholar
  22. 22.
    Refaeilzadeh, P., Tang, L., Liu, H.: On comparison of feature selection algorithms. In: AAAI 2007 Workshop on Evaluation Methods for Machine Learning II, Vancouver, Canada, pp. 1–6 (2007)Google Scholar
  23. 23.
    Ribeiro, M.X., Balan, A.G.R., Felipe, J.C., Traina, A.J.M., Traina Jr., C.: Mining statistical association rules to select the most relevant medical image features. In: 1st Intl. Workshop on Mining Complex Data (IEEE MCD 2005), Houston, USA, pp. 91–98 (2005)Google Scholar
  24. 24.
    Ribeiro, M.X., Marques, J., Traina, A.J.M., Traina-Jr, C.: Statistical association rules and relevance feedback: Powerful allies to improve the retrieval of medical images. In: 19th IEEE Intl. Symposium on Computer-Based Medical Systems, Salt Lake City, USA, pp. 887–892 (2006)Google Scholar
  25. 25.
    Ribeiro, M.X., Vieira, M.T.P.: A new approach for mining association rules in data warehouses. In: Christiansen, H., Hacid, M.-S., Andreasen, T., Larsen, H.L. (eds.) FQAS 2004. LNCS (LNAI), vol. 3055, pp. 28–110. Springer, Heidelberg (2004)Google Scholar
  26. 26.
    Savarese, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: 21st Conf. on Very Large Databases (VLDB 1995) (1995)Google Scholar
  27. 27.
    Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: ACM SIGMOD Intl. Conf. on Management of Data, Montreal, Canada, pp. 1–12 (1996)Google Scholar
  28. 28.
    Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, Newport Beach, USA (1997)Google Scholar
  29. 29.
    Zhang, S., Wu, X., Zhang, C.: Multi-database mining. IEEE Computational Intelligence Bulletin 2(1), 5–13 (2003)Google Scholar
  30. 30.
    Zhong, N., Ohshima, M., Yao, Y.Y., Ohsuga, S.: Interestingness, peculiarity, and multi-database mining. In: IEEE Intl. Conf. on Data Mining, pp. 566–573 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marcela X. Ribeiro
    • 1
  • Andre G. R. Balan
    • 1
  • Joaquim C. Felipe
    • 2
  • Agma J. M. Traina
    • 1
  • Caetano TrainaJr.
    • 1
  1. 1.Department of Computer ScienceUniversity of São Paulo at São CarlosBrazil
  2. 2.Department of Physics and MathematicsUniversity of São PauloBrazil

Personalised recommendations