Mining multi-dimensional data for decision support

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While it is widely recognized that data can be a valuable resource for any organization, extracting information contained within the data is often a difficult problem. Attempts to obtain information from data may be limited by legacy data storage formats, lack of expert knowledge about the data, difficulty in viewing the data, or the volume of data needing to be processed. The rapidly developing field of Data Mining or Knowledge Data Discovery is a blending of Artificial Intelligence, Statistics, and Human-Computer Interaction. Sophisticated data navigation tools to obtain the information needed for decision support do not yet exist. Each data ... continued below

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9 p.

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Donato, J.M.; Schryver, J.C.; Hinkel, G.C.; Schmoyer, R.L. Jr.; Grady, N.W. & Leuze, M.R. June 1, 1998.

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Description

While it is widely recognized that data can be a valuable resource for any organization, extracting information contained within the data is often a difficult problem. Attempts to obtain information from data may be limited by legacy data storage formats, lack of expert knowledge about the data, difficulty in viewing the data, or the volume of data needing to be processed. The rapidly developing field of Data Mining or Knowledge Data Discovery is a blending of Artificial Intelligence, Statistics, and Human-Computer Interaction. Sophisticated data navigation tools to obtain the information needed for decision support do not yet exist. Each data mining task requires a custom solution that depends upon the character and quantity of the data. This paper presents a two-stage approach for handling the prediction of personal bankruptcy using credit card account data, combining decision tree and artificial neural network technologies. Topics to be discussed include the pre-processing of data, including data cleansing, the filtering of data for pertinent records, and the reduction of data for attributes contributing to the prediction of bankruptcy, and the two steps in the mining process itself.

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9 p.

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OSTI as DE98003512

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  • High-performance computing and networking `98, Amsterdam (Netherlands), 21-23 Apr 1998

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  • Other: DE98003512
  • Report No.: ORNL/CP--97722
  • Report No.: CONF-980444--
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 661671
  • Archival Resource Key: ark:/67531/metadc709961

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Office of Scientific & Technical Information Technical Reports

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  • June 1, 1998

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • Jan. 19, 2016, 7:50 p.m.

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Donato, J.M.; Schryver, J.C.; Hinkel, G.C.; Schmoyer, R.L. Jr.; Grady, N.W. & Leuze, M.R. Mining multi-dimensional data for decision support, article, June 1, 1998; Tennessee. (digital.library.unt.edu/ark:/67531/metadc709961/: accessed December 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.