Indian Streams Research Journal

Volume : IV, Issue : I, February - 2014

CONCEPT OF VISUALIZATION USING DATA MINING TECHNIQUES

Vinayak B. Kotmir, None

DOI : 10.9780/22307850, By : Laxmi Book Publication

Abstract :

t:-Visualization is known to be one of the most efficient data mining approaches. The human eye can capture complex patterns and relationships, along with detecting the outlying (exceptional) cases in a data set. The main limitation of the visual data analysis is its poor scalability: it is hardly applicable to data sets of high dimensionality. We use the concepts of Fuzzy Set Theory to automate the process of human perception. The automated tasks include comparison of frequency distributions, evaluating reliability of dependent variables, and detecting outliers in noisy data. Multiple perceptions (related to different users) can be represented by adjusting the parameters of the fuzzy membership functions. The applicability of automated perceptions is demonstrated on several real-world data sets.

Keywords :

• data visualization
• Data mining
• Fuzzy set theory
• data perception
• rule extraction.
• data visualization
• Data mining
• Fuzzy set theory
• data perception
• rule extraction.

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References :

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24. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds., AAAI/MIT Press, Menlo Park, CA pp. 1-30, 1996.
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26. C. Brunk, J. Kelly, R. Kohavi, Mineset: An Integrated System for Data Mining, Proc. of the 3rd International Conf. On Knowledge Discovery and Data Mining, Menlo Park, CA, 1997.
27. Informative Patterns and Data Cleaning, In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds AAAI/MIT Press, Menlo Park, CA, pp. 181-203, 1996.
28. I. Guyon, N. Matic, and V. Vapnik, Discovering
29. C. Brunk, J. Kelly, R. Kohavi, Mineset: An Integrated System for Data Mining, Proc. of the 3rd International Conf. On Knowledge Discovery and Data Mining, Menlo Park, CA, 1997.
30. Informative Patterns and Data Cleaning, In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds AAAI/MIT Press, Menlo Park, CA, pp. 181-203, 1996.
31. J.F. Elder IV and D. Pregibon, A Statistical Perspective on Knowledge Discovery in Databases. In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds., AAAI/MIT Press, Menlo Park, CA, pp. 83-113, 1996.
32. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds., AAAI/MIT Press, Menlo Park, CA pp. 1-30, 1996.
33. A. Kandel, R. Pacheco, A. Martins, and S. Khator, The Foundations of Rule-Based Computations in Fuzzy Models. In Fuzzy Modelling, Paradigms and Practice W. Pedrycz, Ed., Kluwer, Boston, pp. 231-263, 1996.
34. Informative Patterns and Data Cleaning, In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds AAAI/MIT Press, Menlo Park, CA, pp. 181-203, 1996.
35. I. Guyon, N. Matic, and V. Vapnik, Discovering
36. C. Brunk, J. Kelly, R. Kohavi, Mineset: An Integrated System for Data Mining, Proc. of the 3rd International Conf. On Knowledge Discovery and Data Mining, Menlo Park, CA, 1997.

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