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A Refined Continuous Ant Colony Optimization Based FP-Growth Association Rule Technique on Type 2 Diabetes

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In recent years, Diabetes mellitus almost tops the list of chronic diseases worldwide among the major public health challenges. Diagnosing diabetes at the preliminary stage is undoubtedly challenging as it involves varying complexities and inter relation and dependence on several factors that affect it directly or indirectly. Due to large number of diabetic patients in recent years, desperate measures have to be devised and developed to facilitate medical diagnostic decision support systems that help doctors, researchers and medical practitioners during and after the process of diagnosing Diabetes. In this paper, the Association Rule Mining and Enhanced FP-Growth Algorithm has been used as a reference to propose a new algorithm that is basically has similar functionalities as that of the Ant Colony Optimization algorithm and one that improvises association rule mining results. The Refined Continuous Ant Colony Optimization or CACO deploys a meta-heuristic approach and has been devised and enthused by actual ant colonies behavior along with the sustained continuous domains. Preliminarily association rules so produced by the Enhanced FP-Growth algorithm are deployed thereafter which rules from weakest set are found on the basis of threshold value and then further used by the Ant Colony algorithm so that association rules are reduced and a better quality of rules are discovered as a result of the efforts. The study as well the research presented here aims at reducing database scanning by optimizing as well by improving the quality of rules that are produced for CACO.
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Data Mining; Data Discretizing; Association Rule Mining (ARM); Apriori Algorithm; Ant Colony Optimization (ACO); Continuous Ant Colony Optimization; Enhanced FP-Growth; Type 2 Diabetes Mellitus (DM)

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Ramachandran A, Mary S, Sathish CK, Selvam S, Catherin Seeli A. et al Population Based Study of Quality of Diabetes Care in Southern India. Journal of Association of Physicians of India, 2008, 56:513–516.

IDF Diabetes Atlas. The Economic Impacts of Diabetes. Available from: http://www.diabetesatl as. org/content/economic-impacts - diabetes. Accessed April 1, 2011.

Milan Z, Gou M, Peter K et al. Mining diabetes database with decision trees and association rules. In: Proceedings of the 15 th IEEE Symposium on Computer- Based Medical Systems, 2002, pp. 867—871.

J.Han, and M.Kamber, Data mining: Concepts and techniques, San Francisco: Morgan Kaufmann Publisher, pp.47- 94, 2006.

B. M. Patil, R. C. Joshi, Durga Toshniwal, Association rule for classification of type -2 diabetic patients, In Proceedings of 2010 Second International Conference on Machine Learning and Computing, pp. 330–334.

Santhi, R. and Vanitha, K., (April-2012) An Effective Association Rule Mining in Large Database, International Journal of Computer Application and Engineering Technology, 1(2), ISSN: 2277-7962, pp. 72-76., September 2012.

AK Jain, MN Murthy, PJ Flynn,' Data clustering- A review', ACM Computing Surveys, Oct. 2001

Karla Taboada, S Mabu, E Gonzales, Genetic Network programming for fuzzy association rule based classification, 2009.

Barker, T., Haartman, M., (2005) Ant Colony Optimization, IEEE 516 Spring.

Santhi, R. and Vanitha, K., (April-2012) An Effective Association Rule Mining in Large Database, International Journal of Computer Application and Engineering Technology, 1(2), ISSN: 2277-7962, pp. 72-76.

Dhanda, M., Guglani, S., Gupta, G. (Sep-2011) Mining Efficient Association Rules Through Apriori Algorithm Using Attributes, International Journal Computer Science and Technologies, 2(3), ISSN: 2229-4333 (print), ISSN: 0976-8491 (online), pp. 342-344.

Singh, J., Ram, H., and Sodhi, J., (Jan-2013) Improving Efficiency of Apriori Algorithm Using Transaction Reduction, International Journal of Scientific and Research Publications, 3(1), ISSN: 2250-3153, pp. 1-4.

Srinivas, K., Rao, G., Govardhan, A., (2012) Mining Association Rules from Large Datasets towards Disease Prediction, International Conference on Information and Computer Networks, Vol. 27, pp. 22-26.

Pradeepini G., S. Jyothi, Tree-Based Incremental Association Rule Mining without Candidate Itemset Generation, Trendz in Information Sciences & Computing (TISC), pp. 78-81, IEEE 2010.

Liu Jian-ping, Wang Ying, Yang Fan-ding, Incremental-Mining algorithm Pre-FP in association rules based on FP-tree, Networking and Distributed Computing (ICNDC), First international Conference, pp.199-203, IEEE 2010.

T. P. Hong, J. W. Lin, and Y. L. Wu, Maintenance of fast updated frequent pattern trees for record modification, The International Conference on Innovative Computing, Information and Control, pp. 570-573, IEEE 2006.

Siqing Shan, Xiaojing Wang, and Miao Sui, Mining Association Rules: A Continuous Incremental Updating Technique, 39In proceeding of: Web Information Systems and Mining (WISM), International Conference on, Volume: 1, pp. 62 – 66, IEEE 2010.

Liu Han-bing, Zhang Ya-juan, Zheng Quan-lu, Ye Mao-gong, New Incremental Updating Algorithm for Mining Association Rules Based on AprioriTidList Algorithm, Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), Vol. 2, pp 1611 – 1614, IEEE 2011.

T.Karthikeyan, R.Ragavan, K.Vembandasamy, Hierarchical K-Means Clustering Algorithm for an E-Care of Diabetes Mellitus, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 12, December 2011,pp. 653-660.

Asha, P., Jebarajan, T., Improved parallel pattern growth data mining algorithm, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 80-87.

Sathesh Kumar, K., Hemalatha, M., An optimized inference of pattern recognition using Fuzzy Ant Based Clustering Algorithm, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 54-63.


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