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International Conference on Database and Expert Systems Applications

DEXA 2020: Database and Expert Systems Applications pp 177–187Cite as

Algebra for Complex Analysis of Data

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12391)

Abstract

In data science, the process of development focuses on the improvement of methods for individual data analytical tasks. However, their combination is not properly researched. We believe that this situation is caused by a missing framework, that would focus solely on data analytical tasks, instead of complicated transformation between individual methods. In this paper, a new analytical algebra is defined. This algebra is based on a flat structure of transaction file and operations over it. As a part of the paper, definitions of several data analytical tasks are proposed. Algebra is recursive and extendable. As an example of usability of the algebra, one complex analytical task created by a combination of analytical operators is described.

Keywords

  • Data analysis
  • Analytical algebra
  • Similarity
  • Pattern mining

This research has been supported by the GACR project No. GA19-02033S.

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References

  1. Aggarwal, C.C., Han, J. (eds.): Frequent Pattern Mining. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07821-2

    CrossRef  MATH  Google Scholar 

  2. Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB. vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  3. Aryabarzan, N., Minaei-Bidgoli, B., Teshnehlab, M.: negFIN: an efficient algorithm for fast mining frequent itemsets. Expert Syst. Appl. 105, 129–143 (2018)

    CrossRef  Google Scholar 

  4. Chakraborty, T., Dalmia, A., Mukherjee, A., Ganguly, N.: Metrics for community analysis: a survey. ACM Comput. Surv. (CSUR) 50(4), 1–37 (2017)

    CrossRef  Google Scholar 

  5. Ciaccia, P., Patella, M., Zezula, P.: M-tree: An E cient access method for similarity search in metric spaces. In: Proceedings of the 23rd VLDB Conference, Athens, Greece, pp. 426–435. Citeseer (1997)

    Google Scholar 

  6. Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: SPMF: a java open-source pattern mining library. J. Mach. Learn. Res. 15, 3569–3573 (2014). http://jmlr.org/papers/v15/fournierviger14a.html

  7. Gupta, M.K., Chandra, P.: A comparative study of clustering algorithms. In: 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 801–805. IEEE (2019)

    Google Scholar 

  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)

    CrossRef  Google Scholar 

  9. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29, 1–12 (2000)

    Google Scholar 

  10. Kluyver, T., et al.: Jupyter notebooks-a publishing format for reproducible computational workflows. In: ELPUB, pp. 87–90 (2016)

    Google Scholar 

  11. Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Data sets. Cambridge university press, New York (2020)

    Google Scholar 

  12. MATLAB: version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts (2010)

    Google Scholar 

  13. Mitzenmacher, M., Pagh, R., Pham, N.: Efficient estimation for high similarities using odd sketches. In: Proceedings of the 23rd International Conference on World Wide web, pp. 109–118 (2014)

    Google Scholar 

  14. Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 647–652. ACM (2004)

    Google Scholar 

  15. Novak, D., Batko, M., Zezula, P.: Metric index: an efficient and scalable solution for precise and approximate similarity search. Inf. Systems 36(4), 721–733 (2011)

    CrossRef  Google Scholar 

  16. Pei, J., et al.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of 17th International Conference on Data Engineering, pp. 215–224. IEEE (2001)

    Google Scholar 

  17. Peschel, J., Zezula, P.: ADAMiSS: advanced data analysis, mining and search, system. In: Amato, G., Gennaro, C., Oria, V., Radovanović, M. (eds.) SISAP 2019. LNCS, vol. 11807, pp. 351–355. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32047-8_31

    CrossRef  Google Scholar 

  18. Plantié, M., Crampes, M.: Survey on social community detection. In: Ramzan, N., van Zwol, R., Lee, J.S., Clúver, K., Hua, X.S. (eds.) Social Media Retrieval. CCN, pp. 65–85. Springer, Lodon (2013). https://doi.org/10.1007/978-1-4471-4555-4_4

    CrossRef  Google Scholar 

  19. Schubert, E., Zimek, A.: ELKI: a large open-source library for data analysis - ELKI release 0.7.5 “heidelberg”. CoRR abs/1902.03616 (2019). http://arxiv.org/abs/1902.03616

  20. Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0014140

    CrossRef  Google Scholar 

  21. Team, R.C., et al.: R: a language and environment for statistical computing (2013)

    Google Scholar 

  22. Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings, pp. 721–724. IEEE (2002)

    Google Scholar 

  23. Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. Masaryk University, Brno, Czech Republic

    Jakub Peschel, Michal Batko & Pavel Zezula

Authors
  1. Jakub Peschel
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  2. Michal Batko
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  3. Pavel Zezula
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Corresponding author

Correspondence to Jakub Peschel .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Prof. Dr. Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. IFS, Vienna University of Technology, Vienna, Wien, Austria

    Prof. A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Peschel, J., Batko, M., Zezula, P. (2020). Algebra for Complex Analysis of Data. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12391. Springer, Cham. https://doi.org/10.1007/978-3-030-59003-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-59003-1_12

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