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Optimization of Association Rule Mining for Mammogram Classification
Poonam Niraj Sonar, Udhav Bhosle
Pages - 67 - 84     |    Revised - 30-04-2017     |    Published - 01-06-2017
Volume - 11   Issue - 3    |    Publication Date - June 2017  Table of Contents
Association Rule Mining, Support, Confidence, Multi Objective Fitness Function, Adaptive Mutation, Crossover, Graph Theory, Genetic Algorithm.
Authors presents concept of image mining, an extension of data mining, for discovering image data relationship from a large collection of mammograms images. Association rule mining is the process of discovering useful and interesting rules from large datasets based on user specified minimum support and confidence values. The set of all possible item sets grows exponentially with the number of items in the database. These constraints lead to exponential search space and dataset dependent minimum support and confidence values. It generates a huge number of unnecessary rules from frequent item sets and results in weak mining performance. The authors propose two association rule optimization techniques for overcoming these problems. The first graph theory approach (OARGT) is based on objective function such that graph generated by the optimized rules is a simple graph with simple walk. The second approach is based on Multi-Objective Genetic algorithm (MOGA) with adaptive crossover and mutations (MOGAACM). Traditionally, input to ARM classifier is in binary format. The proposed system uses variable feature quantization and relevant feature selection. In MOAGAACM, ranks are assigned to rules as per fitness function. The rules with highest rank, low crossover and mutation rates are assigned and vice versa. Experimental results show that, MOGAACM generates more effective and strong association rules compared with objective function using graph theory and achieves 89.08% and 43.60 % reduction in association rules for benign and malignant class respectively for MIAS database and 80.13 % and 79.60 % reduction in association rules for benign and malignant class respectively for DDSM medical image database respectively. The Graph theory achieves 9 % reduction in rules for MIAS data base. Authors propose class identification using Strength of Classification Algorithm (CISCA) for classification of mammogram image into benign and malignant classes. The classification accuracy measures reported are 91.66 % for MIAS database using graph theory and 95.45 % and 92.5 % for MIAS and DDSM respectively using MOGAACM.
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1 J. Zhang, W. Hsu and M. L. Lee. "Image Mining: Trends and Developments." Journal of Intelligent Information Systems, 19:1, pp. 7-23, 2002.
2 R. Agrawal et al., "Mining association rules between sets of items in large databases", in proceedings of the ACM SIGMOD ICMD, Washington DC,1993, pp 207-216
3 R. M. Haralick et al.," Textural features for image classification", IEEE Trans Syst. Man. Cybern., Vol. SMC-3.610-621, 1973.
4 M. X. Ribeiro, J. M. Agni,T. Caetano , and P. M. Azevedo-Marques, "An Association Rule-Based method to support medical images diagnosis with efficiency.", IEEE Transactions on Multimedia, Vol.10, No. 2, pp. 277-285, February 2008.
5 A. K. Mohanty, S. K. Swain, P. K. Champati, S. K. Lenka, "Image Mining for Mammogram Classification by Association Rule Using Statistical and GLCM features", International Journal of Computer Science Issue, 2011.
6 M. L. Antonie, R. Osmar, Zaiane, C. Alexandru, "Mammography classification by an association rule-based classifier", in Proceedings of Third International Workshop on Multimedia Data Mining, 2002, pp. 62-69.
7 M. L. Antonie, R. Osmar, Zaiane, C. Alexandru, "Application of Data Mining Techniques for Medical Image Classification", in Proceedings of Second International Workshop on Multimedia Data Mining (MDM/KDD'2001) in conjunction with ACM SIGKDD conference, San Francisco, USA, Aug 26, 2001; pp. 94-101.
8 A. Dhawan et al.," Analysis of mammographic micro calcification using gory scales level image structure features, IEEE Transactions on medical imaging, 15(3), 246-259, 1996
9 B. Pratibha, B. Sadhashivam, "Brest Tissue characterization using variant of nearest neighbor classifier in multi texture domain", IE(1), J91, pp 7-13, 2010.
10 A. Ghosh, B. Nath," Multi-objective rule mining using genetic algorithms", Information Sciences, Elsevier, Vol.163, pp.123-133, 2004.
11 X. Yan, C. Zhang and S. Zhang," Genetic Algorithms based strategy for identifying association rules without specifying actual minimum support", Expert System with Applications, Science Direct, 36(2009) page 3066-76.
12 S. Kannimuthu, K. Premalatha, "Discovery of high utility item sets using genetic algorithm with ranked mutation. Appl. Artificial Intelligence. 28(4), 337-359 (2014)
13 M. U. Bandyopadhyay and S. Mukhopadhyay," A Survey of Multi objective Evolutionary for data mining", IEEE Transactions E Vol, Commutating, 18(1),4-19, 2014.
14 R. D. Martin, A. Fedz," A new Multi objective Evolutionary Algorithms for mining a reduced set of Interesting Positive and negative quantities Association rules", IEEE Transactions Evolutionary Commutating 18(1),54-69, (2014)
15 U. Maulik, S. Bandyopadhyay and A. Mukhopadhyay, "Multiobjective Genetic Algorithms for Clustering Applications in Data mining and Bioinformatics", Springer, Heidelberg-Berlin, ISBN 978-3-642-16614-.
16 P. Shenoy, K. Srinivas, K. Venugopal and L. Patnaik, "Dynamic Association rule mining using Genetic Algorithms ", Intel data Analysis, 9(5), 439-453 (2005).
17 K. Premalatha and A. M. Natarajan," Genetic Algorithms for document Clustering with Simultaneous and ranked Mutation", Journal of Modern Applied Science, 3(2), 2009, Page 75-82.
18 S. Dehuri, A. Ghosh, R. Mall," Genetic algorithm for Multi-criterion classification and clustering in Data mining", International Journal of Computing and Information Science, Vol 4, No. 3, Pages 143-154.
19 Mir Md. J. Kabir, X. Shuxiang, B. Ho Kang, and Zongyuan Zhao," Discovery of Interesting Association Rules Using Genetic Algorithm with Adaptive Mutation", ICONIP 2015, part II, LNCS, pp 96-105, 2015.
20 J. Han, M. Kamber, J. Pei, "Data Mining Concepts and Techniques", Morgan Kaufmann publishers, 2012.
21 L.M. Bruce, R.R. Adhami, classifying mammographic mass shapes using the wavelet transform modulus-maxima method, IEEE Trans. Med. Imaging 18 (1999) 1170-1177
22 D. Sumeet, S. Harpreet, H.W. Thompson, Associative classification of mammograms using weighted rules, Expert Systems with Applications, ELSEVIER, (2009), 36, 9250-9259
23 D.Wang, Shi L, Ann Heng P, Automatic detection of breast cancers in mammograms using structured support vector machines, Neurocomputing2009; 72: 3296-3302.
24 W.B. Sampaio, Diniz EM, Silva AC, de Paiva AC, Gattass M." Detection of masses in mammogram images using CNN, geostatistical functions and SVM. 653-664 Article in Computers in Biology and Medicine 41(8):653-64. June 2011
25 D. S. Deshpande, A. M. Rajurkar and R. M. Manthalkar, "Medical image analysis an attempt for mammogram classification using texture based association rule mining," 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Jodhpur, 2013, pp. 1-5.
26 C. Zhili, H. Strange, A. Oliver et al., "Topological Modeling and Classification of Mammographic Macrocalcification Clusters", IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol. 62, No. 4, April 2015, pp. 1203-1214.
27 H. Gisele, M. Miranda, C. Joaquim ", Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization", Computers in Biology and Medicine 64 (2015) 334-346, Elsevier.
28 L. Yanfeng et al.," Textron analysis for mass classification in mammograms", Pattern Recognition Letters, Volume 52, pp. 87-93
29 M. Kanchanamani, P. Perumal, "Performance evaluation and comparative analysis of various machine learning techniques for diagnosis of breast cancer", proceedings of biomedical research 2016.
30 J. Wang, Yang X, Cai H, Tan W, Jin C, Li L., "Discrimination of Breast Cancer with Macrocalcifications on Mammography by Deep Learning". Scientific Reports. 2016; 6:27327.
Mrs. Poonam Niraj Sonar
Research Scholar, Department of Electronics and Telecommunication Engineering, Rajiv Gandhi Institute of Technology, Mumbai. University of Mumbai, India - India
Mr. Udhav Bhosle
Department of Electronics and Telecommunication Engineering, Rajiv Gandhi Institute of Technology, Mumbai, University of Mumbai, India - India