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Ant Colony Based Optimization from Infrequent Itemsets

  • Conference paper
Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 327))

Abstract

Data Mining is the area of research by which we can find relevant patterns from the data set. It is used in several areas. In this paper we are focusing on finding relevant patterns from Positive and Negative Rules. For this we have applied Ant Colony Optimization (ACO) technique on the positive and negative rules. Our algorithm has achieved better global optimum value and chances of finding are improved. So the chances of Positive or relevant rules are more in comparison to the traditional technique. We are also applying the optimization to the negative rules so that there are equal chances for achieving the global optimum. But the negative rules are not qualifying the global optimum value and hence the relevant rules find by our algorithm are verified.

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References

  1. Sweeney, L.: K-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  2. Agrawal, R., Srikand, R.: Privacy preserving data mining. In: Proc. of ACM SIGMOD Conference, pp. 439–450 (2000)

    Google Scholar 

  3. Chen, K., Liu. L.: A random rotation perturbation approach to privacy data classification. In: Proc. of IEEE Intl. Conf. on Data Mining (ICDM), pp. 589–592 (2005)

    Google Scholar 

  4. Xu, S., Zhang, J., Han, D., Wang, J.: Singular value decomposition based data distortion strategy for privacy distortion. Knowledge and Information System 10(3), 383–397 (2006)

    Article  Google Scholar 

  5. Mukherjeee, S., Chen, Z., Gangopadhyay, A.: A privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier related transforms. Journal of VLDB 15(4), 293–315 (2006)

    Article  Google Scholar 

  6. Vaidya, J., Clifton, C.: Privacy preserving k-means clustering over vertically partitioned data. In: Prof. of ACM SIGKDD Conference, pp. 206–215 (2003)

    Google Scholar 

  7. Vaidya, J., Yu, H., Jiang, X.: Privacy preserving SVM classification. Knowledge and Information Systems 14, 161–178 (2007)

    Article  Google Scholar 

  8. Dubey, A.K., Shandilya, S.K.: A novel J2ME service for mining incremental patterns in mobile computing. In: Das, V.V., Vijaykumar, R. (eds.) ICT 2010. Communications in Computer and Information Science, vol. 101, pp. 157–164. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Dubey, A.K., Kushwaha, G.R., Shrivastava, N.: Heterogeneous data mining environment based on DAM for mobile computing environments. In: Das, V.V., Thomas, G., Lumban Gaol, F. (eds.) AIM 2011. CCIS, vol. 147, pp. 144–149. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Dubey, A.K., Dubey, A.K., Agarwal, V., Khandagre, Y.: Knowledge Discovery with a Subset-Superset Approach for Mining Heterogeneous Data with Dynamic Support. In: Conseg-2012 (2012)

    Google Scholar 

  11. Jain, N., Sharma, V., Malviya, M.: Reduction of Negative and Positive Association Rule Mining and Maintain Superiority of Rule Using Modified Genetic Algorithm. International Journal of Advanced Computer Research (IJACR) 2(4(6)) (December 2012)

    Google Scholar 

  12. Khare, P., Gupta, H.: Finding Frequent Pattern with Transaction and Occurrences based on Density Minimum Support Distribution. International Journal of Advanced Computer Research (IJACR) 2(3(5)) (September 2012)

    Google Scholar 

  13. Deshpande, L.A., Prasad, R.S.: Efficient Frequent Pattern Mining Techniques of Semi Structured data: a Survey. International Journal of Advanced Computer Research (IJACR) 3(1(8)) (March 2013)

    Google Scholar 

  14. Jiang, H., Zhao, Y., Dong, X.: Mining Positive and Negative Weighted Association Rules from Frequent Itemsets Based on Interest, Computational Intelligence and Design. In: International Symposium on Computational Intelligence and Design, ISCID 2008, 17-18 October, vol. 2, pp. 242–245 (2008)

    Google Scholar 

  15. Zhao, Y., Jiang, H., Geng, R., Dong, X.: Mining Weighted Negative Association Rules Based on Correlation from Infrequent Items. In: International Conference on Advanced Computer Control,ICACC 2009, January 22-24, pp. 270–273 (2009)

    Google Scholar 

  16. Ouyang, W., Huang, Q.: Mining direct and indirect fuzzy association rules with multiple minimum supports in large transaction databases. In: Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), July 26-28, vol. 2, pp. 947–951 (2011)

    Google Scholar 

  17. Zhong, Y., Liao, Y.: Research of Mining Effective and Weighted Association Rules Based on Dual Confidence. In: Fourth International Conference on Computational and Information Sciences (ICCIS), August 17-19, pp. 1228–1231 (2012)

    Google Scholar 

  18. Jiang, H., Luan, X., Dong, X.: Mining Weighted Negative Association Rules from Infrequent Itemsets Based on Multiple Supports. In: International Conference on Industrial Control and Electronics Engineering (2012)

    Google Scholar 

  19. Gosain, A., Bhugra, M.: A Comprehensive Survey of Association Rules On Quantitative Data in Data Mining. In: IEEE Conference on Information and Communication Technologies (2013)

    Google Scholar 

  20. Ouyang, W., Huang, Q.: Mining Direct and Indirect Weighted Fuzzy Association Rules in Large Transaction Databases. In: IEEE Eighth International Conference on Fuzzy Systems and Knowledge Discovery (2011)

    Google Scholar 

  21. Lee, K.-M.: Mining Generalized Fuzzy Quantitative Association Rules with Fuzzy Generalization Hierarchies. IEEE (2011)

    Google Scholar 

  22. Cagliero, L., Garza, P.: Infrequent Weighted Itemset Mining using Frequent Pattern Growth. IEEE Transactions on Knowledge and Data Engineering (2013)

    Google Scholar 

  23. Chiang, J.K., Huang, S.-Y.: Multidimensional Data Mining for Healthcare Service Portfolio Management. IEEE (2013)

    Google Scholar 

  24. Rameshkumar, K., Sambath, M., Ravi, S.: Relevant Association Rule Mining from Medical Dataset Using New Irrelevant Rule Elimination Technique. IEEE (2013)

    Google Scholar 

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Authors and Affiliations

  1. NIIST, Bhopal, M.P., India

    Diti Gupta & Abhishek Singh Chauhan

Authors
  1. Diti Gupta
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  2. Abhishek Singh Chauhan
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Bhubaneswar Engineering College, Bhubaneswar, Odisha, India

    Bhabendra Narayan Biswal

  3. University of Hyderabad, Hyderabad, Andhra Pradesh, India

    Siba K. Udgata

  4. Department of Computer Science and Engineering, University of Kalyanai, Faculty of Engg., Tech. & Management, Kalyanai, West Bengal, India

    J.K. Mandal

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Gupta, D., Chauhan, A.S. (2015). Ant Colony Based Optimization from Infrequent Itemsets. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_90

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  • DOI: https://doi.org/10.1007/978-3-319-11933-5_90

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11932-8

  • Online ISBN: 978-3-319-11933-5

  • eBook Packages: EngineeringEngineering (R0)

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