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Weighted Frequent Itemset Mining Using OWA on Uncertain Transactional Database

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Data Communication and Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1049))

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Abstract

The technology of data mining has a broad scope in banking, manufacturing, medical science, and business decision-making. In these applications, the most commonly used term is Frequent Itemset Mining Algorithms which is one of the vital parts of Association Rule Mining. The evolutionary improvement in FIM is Weighted Frequent Itemset Mining, and these algorithms can be executed on Certain/Probabilistic Uncertain databases for calculating important frequent itemsets, having weight and expected support equal to or greater than user-specified minimum weight or minimum probability, respectively. The weight for an itemset is calculated by taking an average of weights of all items in the itemset. In this case, if the weights of one or two items are very high compared to others than the average can be decided by only higher weights and ignore low values. In this research paper, OWA operator is used in place of the traditional mean for calculating the weight of an itemset. A new algorithm is developed in this research and executed on example database. The results show that the generated itemsets are less in the count but hold more importance.

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References

  1. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in Proceedings of the 20th VLDB Conference, Santiago, Chile (1994), pp. 487–499

    Google Scholar 

  2. J. Han, H. Pei, Y. Yin, Mining frequent patterns without candidate generation, in Proceedings of Conference on the Management of Data (SIGMOD’00, Dallas, TX) (ACM Press, New York, NY, USA, 2000)

    Article  Google Scholar 

  3. M.J. Zaki, Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)

    Article  Google Scholar 

  4. C.K. Chui, B. Kao, E. Hung, Mining frequent itemsets from uncertain data, in 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2007, Nanjing, China

    Google Scholar 

  5. C.K. Chui, B. Kao, A decremental approach for mining frequent itemsets from uncertain data, in PAKDD (2008), pp. 64–75

    Google Scholar 

  6. C.C. Aggarwal, Y. Li, J. Wang, J. Wang, Frequent pattern mining with uncertain data, in Proceedings of ACM KDD (2009), pp. 29–38

    Google Scholar 

  7. C.K.-S. Leung, M.A.F. Mateo, D.A. Brajczuk, A tree-based approach for frequent pattern mining from uncertain data, in Proceedings of PAKDD (2008), pp. 653–661

    Google Scholar 

  8. T. Calders, C. Garboni, B. Goethals, Efficient pattern mining of uncertain data with sampling, in Proceedings of the PAKDD 2010, Part I (Springer, 2010), pp. 480–487

    Google Scholar 

  9. W. Wang, J. Yang, P.S. Yu, Efficient mining of weighted association rules (war), in Proceedings of 6th ACM SIGKDD International Conference on Knowledge Discovery Data Mining (2000), pp. 270–274

    Google Scholar 

  10. F. Tao, F. Murtagh, M. Farid, Weighted association rule mining using weighted support and significance framework, in Proceedings of 9th ACM SIGKDD International Conference on Knowledge Discovery Data Mining (2003), pp. 661–666

    Google Scholar 

  11. U. Yun, J. Leggett, WFIM: weighted frequent itemset mining with a weight range and a minimum weight, in Proceedings of SIAM International Conference on Data Mining (2005), pp. 636–640

    Google Scholar 

  12. U. Yun, G. Lee, K.H. Ryu, Mining maximal frequent patterns by considering weight conditions over data streams. Knowl.-Based Syst. 55(55), 49–65 (2014)

    Article  Google Scholar 

  13. G. Lee, U. Yun, H. Ryang, An uncertainty-based approach: frequent itemset mining from uncertain data with different item importance. Knowl. Based Syst. 90, 239–256 (2015)

    Article  Google Scholar 

  14. A.C.-W. Lin, W. Gan, P. Fournier-Viger, T.-P. Hong, V.S. Tseng, Weighted frequent itemset mining over uncertain databases. Appl. Intell. 44(1), 232–250 (2016)

    Article  Google Scholar 

  15. R.R. Yager, Quantifiers in the formulation of multiple objective decision functions. Inf. Sci. 31, 107–139 (1983)

    Article  MathSciNet  Google Scholar 

  16. R.R. Yager, On a general class of fuzzy connectives. Fuzzy Sets Syst. 4 (1980)

    Article  MathSciNet  Google Scholar 

  17. R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Syst. Man Cybern. 18 (1988)

    Article  MathSciNet  Google Scholar 

  18. S. Wazir, M.M. Sufyan Beg, T. Ahmad, Frequent itemset mining on uncertain database using OWA operator, in Proceedings of 2nd International Conference on Communication, Computing and Networking, ed. by C. Krishna, M. Dutta, R. Kumar. Lecture Notes in Networks and Systems, vol. 46 (Springer, Singapore, 2019)

    Google Scholar 

  19. X. Zhao, X. Zhang, P. Wang, S. Chen, Z. Sun, A weighted frequent itemset mining algorithm for intelligent decision in smart systems. IEEE Access 6, 29271–29282 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Jamia Millia Islamia, New Delhi, 110025, Delhi, India

    Samar Wazir & Tanvir Ahmad

  2. Department of Computer Engineering, Aligarh Muslim University, Aligarh, UP, 202001, India

    M. M. Sufyan Beg

Authors
  1. Samar Wazir
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  2. M. M. Sufyan Beg
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  3. Tanvir Ahmad
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Corresponding author

Correspondence to Samar Wazir .

Editor information

Editors and Affiliations

  1. Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW, Australia

    Lakhmi C. Jain

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  3. Department of Automation and Applied Informatics, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina E. Balas

  4. Department of Computer Engineering and Applications, Institute of Engineering and Technology, GLA University, Mathura, India

    Dilip Kumar Sharma

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Wazir, S., Sufyan Beg, M.M., Ahmad, T. (2020). Weighted Frequent Itemset Mining Using OWA on Uncertain Transactional Database. In: Jain, L., Tsihrintzis, G., Balas, V., Sharma, D. (eds) Data Communication and Networks. Advances in Intelligent Systems and Computing, vol 1049. Springer, Singapore. https://doi.org/10.1007/978-981-15-0132-6_12

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  • DOI: https://doi.org/10.1007/978-981-15-0132-6_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0131-9

  • Online ISBN: 978-981-15-0132-6

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