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Algorithms for Finding and Correcting Four Kinds of Data Mistakes in Information Table

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Book cover Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4251))

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Abstract

In a real world data set there are usually four kinds of mistaken values, the first one is the mistake in unit; the second one is the mistake of putting the radix points in wrong place, the third one is a scribal error, and the fourth one is a computational mistake. In this paper, we propose two algorithms for finding these four kinds of mistaken data. SARS and coronary heart disease data sets experimental results show that the two algorithms are available, that is, using the two algorithms we find some mistakes in the SARS and coronary heart disease data sets, and the results correspond to that found manually by medical experts.

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References

  1. Johnson, Theodore, Dasu, Tamrapami: Data Quality and Data Cleaning: An Overview. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2003)

    Google Scholar 

  2. Zhong-guo, D., Yi-xin, Z.: Research of the Data Cleaning Technique. Journal of Shandong University of Science and Technology (Natural Science) (Chinese) 2, 55–57 (2004)

    Google Scholar 

  3. Simoudis, E., Livezey, B., Kerber, R.: Using Recon for Data Cleaning. In: Proceedings of KDD 1995, pp. 282–287 (1995)

    Google Scholar 

  4. Levitin, A., Redman, T.: A Model of the Data (life) Cycles with Application to Quality. Information and Soft Ware Technology 4, 217–223 (1995)

    Google Scholar 

  5. Jonathan, I., Marcus, M.A.: Data Cleaning: Beyond Integrity Analysis. Division of Computer Science, vol. 2 ( (2000)

    Google Scholar 

  6. Guyon, I., Matic, N., Vapnik, V.: Discovering Information Patterns and Data Cleaning. In: Advances in Knowledge Discovery in Data Ming. MIT Press/ AAAI Press (1996)

    Google Scholar 

  7. Marcus, A., Jonathan, I., Maletic: Utilizing Association Rules for the Identification of Errors in Data. Technical Report. CS-00-04

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Honghai, F., Hao, X., Baoyan, L., LiYun, H., Bingru, Y., Yueli, L. (2006). Algorithms for Finding and Correcting Four Kinds of Data Mistakes in Information Table. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_86

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  • DOI: https://doi.org/10.1007/11892960_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46535-5

  • Online ISBN: 978-3-540-46536-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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