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Comparative Evaluation of the Different Data Mining Techniques Used for the Medical Database Cover

Comparative Evaluation of the Different Data Mining Techniques Used for the Medical Database

Acta Mechanica et Automatica
Volume 10 (2016): Issue 3 (September 2016)
By:  and    
Open Access
|Aug 2016
References

References

  1. 1(1993), Fast algorithm for mining assocation rules,, 487-499.
    Search in Google Scholar
  2. 2(2004), Naive Bayes Classifiers That Perform Well with Continuous Variables,, 3339, 1089-1094.
    Open DOISearch in Google Scholar
  3. 3(2001), Learning Bayesian Belief Network Classifiers,, 141-151.
    Open DOISearch in Google Scholar
  4. 4(2015a), Incomplete distributed information systems optimization based on queries,, Volume 9142 of LNCS Springer, 265-274.
    Open DOISearch in Google Scholar
  5. 5(2013),. Springer, pp.90.
    Open DOISearch in Google Scholar
  6. 6(2003), On Rules Discovery from Incomplete Information Systems,, Melbourne, Florida, IEEE Computer Society.
    Search in Google Scholar
  7. 7(2015b) Queries for detailed information system selection,, Computer Science and Information Systems: FedCSIS, 11-15.
    Open DOISearch in Google Scholar
  8. 8(1994), Rough set based classification methods and extended decision tables,, 302-309.
    Search in Google Scholar
  9. 9(1991), Knowledge discovery in databases,, 1–27.
    Search in Google Scholar
  10. 10(1999), The alternating decision tree algorithm,124-133.
    Search in Google Scholar
  11. 11(2006), Data Mining: Concepts and Techniques,, Second Edition, 21-27.
    Search in Google Scholar
  12. 12(2000), Mining frequent patterns without candidate generation,, 1–12.
    Open DOISearch in Google Scholar
  13. 13(2012), New method for finding rules in incomplete information systems controlled by reducts in flat feet treatment,, 184, 209-214.
    Search in Google Scholar
  14. 14(2011), From Data to Classification Rules and Action,., Wiley, 26(6), 572-590.
    Open DOISearch in Google Scholar
  15. 15(2008), Association Action Rules,, 283-290.
    Open DOISearch in Google Scholar
  16. 16(1997), Query approximate answering system for an incomplete DKBS,, 20(3/4), 313-324.
    Open DOISearch in Google Scholar
  17. 17. (2006), The prevaence and risk factors for occupational voice disorders in teachers,, 58(2), 85-101.
    Search in Google Scholar
  18. 18(2009), Survey of Classification Techniques in Data Mining,, Vol I IMECS.
    Search in Google Scholar
  19. 19(2012), Data mining in healthcare and biomedicine,, 36(4), 2431-2448.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.1515/ama-2016-0036 | Journal eISSN: 2300-5319 | Journal ISSN: 1898-4088
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Language: English
Page range: 233 - 238
Submitted on: Feb 2, 2016
Accepted on: Jul 25, 2016
Published on: Aug 6, 2016
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services
Keywords:
Data Mining,
Classification,
WEKA,
J48,
MLP,
Apriori,
Association Rules
Related subjects:
Engineering,
Mechanical engineering,
Mechanics,
Bioengineering and biomedical engineering,
Biomechanics,
Civil engineering,
Environmental engineering

© 2016 Anna Kasperczuk, Agnieszka Dardzińska, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 10 (2016): Issue 3 (September 2016)
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