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Data Mining with Histograms and Domain Knowledge : Case Studies and Considerations

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An approach to data mining with histograms is introduced. Several examples of applying data mining for various types of histogram are presented. The problem of filtering out uninteresting histograms is described. It is shown that no suitable, logically correct deduction rules to solve this problem exist. Expert deduction rules are introduced as deduction rules supported by indisputable facts, however, incorrect according to mathematical logic. A method for deciding whether a given expert deduction rule is correct according to a given indisputable fact is developed. Applied examples of correct expert deduction rules are described.
Wydawca
Rocznik
Strony
349--378
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr.
Twórcy
autor
  • Faculty of Informatics and Statistics, University of Economics, Prague, nám. W. Churchilla 4, 130 67 Prague 3, Czech Republic
  • Faculty of Informatics and Statistics, University of Economics, Prague, nám. W. Churchilla 4, 130 67 Prague 3, Czech Republic
Bibliografia
  • [1] Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 3rd edition, 2011. ISBN 0123814790, 9780123814791.
  • [2] Tuffery S. Data mining and statistics for decision making. Chichester : John Wiley, 2011. ISBN 9780470688298 (hbk.). Originally published in French :Editions Technip. 2008.
  • [3] Weiss SM, Indurkhya N. Predictive Data Mining: A Practical Guide. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1998. ISBN 1-55860-403-0.
  • [4] Hájek P, Holeňa M, Rauch J. The GUHA method and its meaning for data mining. J. Comput. Syst. Sci., 2010. 76(1):34-48. URL https://doi.org/10.1016/j.jcss.2009.05.004.
  • [5] Rauch J, Šimůnek M. Data Mining with Histograms - A Case Study. In: Foundations of Intelligent Systems - 22nd International Symposium, ISMIS 2015, Lyon, France, October 21-23, 2015, Proceedings. 2015 pp.3-8. doi:10.1007/978-3-319-25252-0_1. URL https://doi.org/10.1007/978-3-319-25252-0_1.
  • [6] Rauch J, Šimůnek M. Apriori and GUHA - Comparing two approaches to data mining with association rules. Intell. Data Anal., 2017. 21(4):981-1013. URL https://doi.org/10.3233/IDA-160069.
  • [7] Rauch J. Logical Aspects of Dealing with Domain Knowledge in Data Mining with Association Rules. Fundam. Inform., 2016. 148(1-2):1-33. URL https://doi.org/10.3233/FI-2016-1420.
  • [8] Rauch J, Šimůnek M. Learning Association Rules from Data through Domain Knowledge and Automation. In: Rules on the Web. From Theory to Applications - 8th International Symposium, Rule ML 2014. Proceedings. 2014 pp. 266-280. URL https://doi.org/10.1007/978-3-319-09870-8_20.
  • [9] Rauch J. Observational Calculi and Association Rules, volume 469 of Studies in Computational Intelligence. Springer, 2013. ISBN 978-3-642-11736-7. URL https://doi.org/10.1007/978-3-642-11737-4.
  • [10] Hájek P, Havránek T. Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer, 1978. ISBN 978-3-642-66943-9. URL https://www.springer.com/gp/book/9783540087380.
  • [11] Font D, Tresánchez M, Palleja T, Martínez D, Moreno J, Palacín J. An image processing method for in-line nectarine variety verification based on the comparison of skin feature histogram vectors. Computers and Electronics in Agriculture, 2014. 102:112-119.
  • [12] Lu Q, Huang X, Liu T, Zhang L. A structural similarity-based label-smoothing algorithm for the post-processing of land-cover classification. Remote Sensing Letters, 2016. 7(5):437-445. doi:10.1080/2150704X.2016.1149252. https://doi.org/10.1080/2150704X.2016.1149252, URL https://doi.org/10.1080/2150704X.2016.1149252.
  • [13] Hung J, Hsieh H, Chen B. Robust Speech Recognition via Enhancing the Complex-Valued Acoustic Spectrum in Modulation Domain. IEEE/ACM Trans. Audio, Speech & Language Processing, 2016. 24(2):236-251. doi:10.1109/TASLP.2015.2504781. URL https://doi.org/10.1109/TASLP.2015.2504781.
  • [14] Shih P, Paul A, Wang J, Chen Y. Speech-driven talking face using embedded confusable system for real time mobile multimedia. Multimedia Tools Appl., 2014. 73(1):417-437. doi:10.1007/s11042-013-1609-3. URL https://doi.org/10.1007/s11042-013-1609-3.
  • [15] Frank E, Hall MA, Witten IH. The WEKA Workbench. https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf. Accessed: 2019-03-02.
  • [16] R Tutorial. http://www.r-tutor.com/elementary-statistics/quantitative-data/histogram. Accessed: 2019-03-02.
  • [17] Rapidminer. https://rapidminer.com/. Accessed: 2019-03-02.
  • [18] Oracle. https://docs.oracle.com/database/121/TGSQL/tgsql_histo.htm#TGSQL366. Accessed: 2019-03-02.
  • [19] Yang Q, Wu X. 10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH. International Journal of Information Technology & Decision Making, 2006. 05(04):597-604. doi:10.1142/S0219622006002258.
  • [20] Rauch J. Expert deduction rules in data mining with association rules: a case study. Knowledge and Information Systems, 2018. doi:10.1007/s10115-018-1206-x.
  • [21] Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94. San Francisco, CA, USA. ISBN 1-55860-153-8, 1994 pp. 487-499. URL http://dl.acm.org/citation.cfm?id=645920.672836.
  • [22] Brossette SE, Sprague AP, Hardin JM, Waites KB, Jones WT, Moser SA. Association Rules and Data Mining in Hospital Infection Control and Public Health Surveillance. Journal of the American Medical Informatics Association, 1998. 5(4):373-381. doi:10.1136/jamia.1998.0050373.
  • [23] Delgado M, Sánchez D, Martín-Bautista MJ, Miranda MAV. Mining association rules with improved semantics in medical databases. Artificial Intelligence in Medicine, 2001. 21(1-3):241-245. doi:10.1016/S0933-3657(00)00092-0.
  • [24] Ordonez C, Ezquerra NF, Santana CA. Constraining and summarizing association rules in medical data. Knowl. Inf. Syst., 2006. 9(3):1-2. doi:10.1007/s10115-005-0226-5.
  • [25] Mansingh G, Osei-Bryson KM, Reichgelt H. Using ontologies to facilitate post-processing of association rules by domain experts. Information Sciences, 2011. 181(3):419-434. doi:https://doi.org/10.1016/j.ins.2010.09.027.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-53068c79-d693-4e48-99ea-e0520968b3a2
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