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Mining Mixed Data Bases Using Machine Learning Algorithms

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Pattern Recognition (MCPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13264))

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Abstract

We discuss numerical algorithms (based on metric spaces) to explore data bases (DB) even though such DBs may not be metric. The following issues are treated: 1. Determination of the minimum equivalent sample, 2. The encoding of categorical variables and 3. Data analysis. We illustrate the methodology with an experimental mixed DB consisting of 29,267 tuples; 15 variables of which 9 are categorical and 6 are numerical. Firstly, we show that information preservation is possible with a (possibly) much smaller sample. Secondly, we approximate the best possible encoding of the 9 categorical variables applying a statistical algorithm which extracts the code after testing an appropriate number of alternatives for each instance of the variables. To do this we solve two technical issues, namely: a) How to determine that the attributes are already normal and b) How to find the best regressive function of an encoded attribute as a function of another. Thirdly, with the transformed DB (now purely numerical) it is possible to find the regressive approximation errors of any attribute relative to another. Hence, we find those attributes which are closer to one another within a predefined threshold (85%). We argue that such variables define a cluster. Now we may use algorithms such as Multi-Layer Perceptron Networks (MLPN) and/or Kohonen maps (SOM). The classification of a target attribute “salary” is achieved with a MLPN. It is shown to yield better results than most traditional conceptual analysis algorithms. Later, by training a SOM for the inferred clusters, we disclose the characteristics of every cluster. Finally, we restore the DB original values and get visual representations of the variables in each cluster, thus ending the mining process.

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References

  1. Kuri-Morales, A.: Pattern discovery in mixed data bases. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A., Sarkar, S. (eds.) MCPR 2018. LNCS, vol. 10880, pp. 178–188. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92198-3_18

    Chapter  Google Scholar 

  2. Rosenblatt, M.: A central limit theorem and a strong mixing condition. Proc. Natl. Acad. Sci. U.S.A. 42(1), 43–47 (1956). https://doi.org/10.1073/pnas.42.1.43

    Article  MathSciNet  MATH  Google Scholar 

  3. https://www.investopedia.com/terms/c/central_limit_theorem.asp. Ganti, Akhilesh, “Central Limit Theorem”

  4. Kaski, S.: Self-Organizing Maps. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8_746

    Chapter  Google Scholar 

  5. https://archive.ics.uci.edu/ml/datasets/census+income

  6. Kuri-Morales, A.F., López-Peña, I.: Normality from monte carlo simulation for statistical validation of computer intensive algorithms. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds.) Advances in Soft Computing: 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Part II, pp. 3–14. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62428-0_1

    Chapter  Google Scholar 

  7. Kuri-Morales, A., Cartas-Ayala, A.: Automatic closed modeling of multiple variable systems using soft computation. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds.) Advances in Soft Computing: 16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Part I, pp. 185–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02837-4_15

    Chapter  Google Scholar 

  8. Cheney, E.W.: Introduction to Approximation Theory, pp. 34–45. McGraw-Hill Book Company (1966)

    Google Scholar 

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Correspondence to Angel Kuri-Morales .

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Kuri-Morales, A. (2022). Mining Mixed Data Bases Using Machine Learning Algorithms. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_7

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  • DOI: https://doi.org/10.1007/978-3-031-07750-0_7

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