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Estimating Prevalence Bounds of Temporal Association Patterns to Discover Temporally Similar Patterns

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Recent Advances in Soft Computing (ICSC-MENDEL 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 576))

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Abstract

Mining Temporal Patterns from temporal databases is challenging as it requires handling efficient database scan. A pattern is temporally similar when it satisfies subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns from temporal databases. The brute force approach requires computing \(2^n\) true support combinations for ‘n’ items from finite item set and falls in NP-class. The apriori or fp-tree based approaches are not directly extendable to temporal databases to obtain similar temporal patterns. In this present research, we come up with novel approach to discover temporal association patterns which are similar for pre-specified subset constraints, and substantially reduce support computations by defining expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. The results prove that the proposed method outperforms brute force approach.

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References

  1. Jin, L., Lee, Y., Seo, S., Ryu, K.H.: Discovery of temporal frequent patterns using TFP-tree. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds.) WAIM 2006. LNCS, vol. 4016, pp. 349–361. Springer, Heidelberg (2006). doi:10.1007/11775300_30

    Chapter  Google Scholar 

  2. Hirano, S., Tsumoto, S.: Mining similar temporal patterns in long time-series data and its application to medicine. In: Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM 2003, pp. 219–226 (2002)

    Google Scholar 

  3. Yoo, J.S., Shekhar, S.: Similarity-profiled temporal association mining. IEEE Trans. Knowl. Data Eng. 21(8), 1147–1161 (2009)

    Article  Google Scholar 

  4. Chen, Y.C., Peng, W.C., Lee, S.Y.: Mining temporal patterns in time interval-based data. IEEE Trans. Knowl. Data Eng. 27(12), 3318–3331 (2015)

    Article  Google Scholar 

  5. Yoo, J.S., Shekhar, S.: Mining temporal association patterns under a similarity constraint. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 401–417. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69497-7_26

    Chapter  Google Scholar 

  6. Yoo, J.S.: Temporal data mining: similarity-profiled association pattern. Data Mining: Foundations and Intelligent Paradigms. Intelligent Systems Reference Library, vol. 23, pp 29–47 (2012)

    Google Scholar 

  7. Radhakrishna, V., Kumar, P.V., Janaki, V.: A Survey on temporal databases and data mining. In: Proceedings of the International Conference on Engineering & MIS 2015 (ICEMIS 2015), Article 52, 6 pages. ACM, New York (2015). doi:10.1145/2832987.2833064

  8. Radhakrishna, V., Kumar, P.V., Janaki, V.: A novel approach for mining similarity profiled temporal association patterns using venn diagrams. In: Proceedings of the International Conference on Engineering & MIS 2015 (ICEMIS 2015), Article 58, 9 pages. ACM, New York (2015). doi:10.1145/2832987.2833071

  9. Radhakrishna, V., Kumar, P.V., Janaki, V.: An approach for mining similarity profiled temporal association patterns using gaussian based dissimilarity measure. In: Proceedings of the International Conference on Engineering & MIS 2015 (ICEMIS 2015), Article 57, 6 pages. ACM, New York (2015). doi:10.1145/2832987.2833069

  10. Radhakrishna, V., Kumar, P.V., Janaki, V.: A novel approach for mining similarity profiled temporal association patterns. Rev. Téc. Ing. Univ. Zulia. 38(3), 80–93 (2015)

    Google Scholar 

  11. Calders, T.: Deducing bounds on the support of itemsets. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds.) Database Support for Data Mining Applications. LNCS (LNAI), vol. 2682, pp. 214–233. Springer, Heidelberg (2004). doi:10.1007/978-3-540-44497-8_11

    Chapter  Google Scholar 

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Authors and Affiliations

  1. VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India

    Vangipuram Radhakrishna

  2. University College of Engineering, Osmania University, Hyderabad, India

    P. V. Kumar

  3. Vaagdevi Engineering College, Warangal, India

    V. Janaki

  4. Dayanandasagar College of Engineering, Bangalore, India

    N. Rajasekhar

  5. Institute of Aeronautical Engineering, Hyderabad, India

    N. Rajasekhar

Authors
  1. Vangipuram Radhakrishna
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  2. P. V. Kumar
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  3. V. Janaki
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  4. N. Rajasekhar
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Corresponding author

Correspondence to Vangipuram Radhakrishna .

Editor information

Editors and Affiliations

  1. Department of Applied Computer Science, Institute of Automation and Computer Science, Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Radhakrishna, V., Kumar, P.V., Janaki, V., Rajasekhar, N. (2017). Estimating Prevalence Bounds of Temporal Association Patterns to Discover Temporally Similar Patterns. In: Matoušek, R. (eds) Recent Advances in Soft Computing. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-319-58088-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-58088-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58087-6

  • Online ISBN: 978-3-319-58088-3

  • eBook Packages: EngineeringEngineering (R0)

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