Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Intl. Conf. on Very Large Databases (VLDB), Santiago, Chile, pp. 487–499 (1994)
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)
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)
Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. Addison-Wesley, Wokingham
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)
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)
Cardie, C.: Using decision trees to improve case-based learning. In: 10th Intl. Conf. on Machine Learning, pp. 25–32 (1993)
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)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Int’l Conf. on Management of Data, Dallas, Texas, USA (2000)
Hsu, W., Lee, M.L., Zhang, J.: Image mining: Trends and developments. Journal of Intelligent Information Systems 19(1), 7–23 (2002)
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)
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)
Kira, K., Rendell, L.A.: A practical approach for feature selection. In: 9th Intl. Conf. on Machine Learning, Aberdeen, Scotland, pp. 249–256 (1992)
Kononenko, I.: Estimating attributes: Analysis and extension of relief. In: European Conf. on Machine Learning, Catania, Italy, pp. 171–182 (1994)
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)
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)
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)
Narendra, P.M., Fukunaga, K.: A branch and bound algorithm for feature subset selection. IEEE Trans. On Computer 26(9), 917–922 (1977)
Ordonez, C., Ezquerra, N., Santana, C.A.: Constraining and summarizing association rules in medical data. Knowledge and Information Systems 9(3), 259–283 (2006)
Quinlan, R.: C4.5: Programs for Machine Learning, San Mateo, CA (1993)
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)
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)
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)
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)
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)
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)
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)
Zhang, S., Wu, X., Zhang, C.: Multi-database mining. IEEE Computational Intelligence Bulletin 2(1), 5–13 (2003)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ribeiro, M.X., Balan, A.G.R., Felipe, J.C., Traina, A.J.M., Traina, C. (2009). Mining Statistical Association Rules to Select the Most Relevant Medical Image Features. In: Zighed, D.A., Tsumoto, S., Ras, Z.W., Hacid, H. (eds) Mining Complex Data. Studies in Computational Intelligence, vol 165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88067-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-88067-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88066-0
Online ISBN: 978-3-540-88067-7
eBook Packages: EngineeringEngineering (R0)