Discovering Optimized Association Rules based on Image Content
DOI:
https://doi.org/10.14738/aivp.42.1994Keywords:
Image Mining, Association rule mining, Correlation measures, Apriori algorithm, mammogram, Genetic algorithmAbstract
Authors present the concept of image mining, an extension of data mining for discovering semantically meaningful information and image data relationship from a large collection of images. Association rule mining is the process of discovering useful and interesting rules, representing frequent patterns from large datasets, depends on user specified minimum support and confidence values. These constraints lead to exponential search space and dataset dependent minimum support and confidence values. The authors propose an optimization technique for overcoming these problems using multi-fitness function Genetic algorithm and constrained nonlinear minimization and minimax optimization method. Synthetic image set containing geometric shapes and standard MIAS medical image dataset are used to validate the proposed optimization algorithm.
Experimental results show that, Genetic algorithm generates more efficient, effective and strong association rules than constrained nonlinear minimization and minimax optimization method. Genetic algorithm achieves 50% and 90%, constrained nonlinear minimization and minimax optimization method achieves 22% and 74 %, reduction in association rules for synthetic image set and standard MIAS medical image dataset respectively.
References
(1) J. Zhang, W. Hsu and M. L. Lee, Image mining: trends and developments. Journal of Intelligent Information Systems, 2002. 19(1): p. 7-23.
(2) C. Ordonez and E. Omiecinski, Discovering association rules based on image content. Research and Technology Advances in Digital Libraries, 1999. Proceedings. IEEE Forum on, IEEE, 1999. p. 38-49.
(3) C. Carson, S. Belongie, H. Greenspan and J. Malik, Region-based image querying. Content-Based Access of Image and Video Libraries, 1997. Proceedings. IEEE Workshop on, IEEE, 1997. p. 42-49.
(4) M. Sahu, M. Shrivastava, Image mining: a new approach for data mining based on texture. IEEE International Conference on Computer and Communication Technology, 2012. p. 7-9.
(5) R. Gonzalez and R. Woods, Digital image processing. Pearson Addison-Wesley Publications Co., Second Edition, March 1992.
(6) J. Nagi, SA. Kareem, F. Nagi, SK. Ahmed, Automated breast profile segmentation for ROI detection using digital mammograms. Biomedical Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on, IEEE, 2010.
(7) RM. Haralick, K. Shanmugam, IH. Dinstein, Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, 1973. 6: P. 610-621.
(8) JC Felipe, AJ Traina, Jr C. Traina, Retrieval by content of medical images using texture for tissue identification. Computer-Based Medical Systems, 2003. Proceedings. 16th IEEE Symposium. IEEE, 2003. p. 175-
(9) K. Beyer, J. Goldstein, R. Ramakrishnan, U. Shaft, When is “nearest neighbor” meaningful?. Database theory—ICDT’99, Springer Berlin Heidelberg, 1999. P. 217-235.
(10) MX. Ribeiro, C. Traina, PM. Azevedo-Marques, An association rule-based method to support medical image diagnosis with efficiency. Multimedia, IEEE Transactions on, 2008. 10(2): p. 277-285.
(11) R. Agrawal, T. Imieliński, A. Swami, Mining association rules between sets of items in large databases. ACM SIGMOD Record, 1993. 22(2): p. 207-216.
(12) PN. Tan, M. Steinbach, V. Kumar, Introduction to data mining. Boston: Pearson Addison Wesley, Jun 2006.
(13) S. N. Sivanandam and S. N. Deepa, Introduction to genetic algorithm. Springer Science and Business media, 2008.
(14) J. Han, M. Kamber and J. Pei, Data mining: concepts and techniques. Elsevier, Jun 2011.
(15) J. Suckling, J. Parker, D. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, S. Kok, P. Taylor, D. Betal, and J. Savage, The mammographic image analysis society digital mammogram database. In IWDM, 1994. P. 211–221.
(16) A. Ghosh and B. Nath, Multi-objective rule mining using genetic algorithms. Information Sciences, Elsevier, 2004. 163(1): p. 123–133.
(17) M. Saggar, AK. Agrawa and A. Lad, Optimization of association rule mining using improved genetic algorithms. IEEE International Conference on Systems, Man and Cybernatics, 2004. p. 3725-3729.
P. P. Wakabi-Waiswa, V. Baryamureeba and K. Sarukesi, Optimized
association rule mining with genetic algorithms. Natural Computation
(ICNC), 2011 Seventh International Conference on, IEEE, 2011. 2: p.
-1120.
