An Integrated Data Mining System to Automate Discovery
Conference Proceeding Article
Many data analysts require tools which can integrate their database management packages (e.g. Microsoft Access) with their data analysis ones (e.g. SAS, SPSS), and provide guidance for the selection of appropriate mining algorithms. In addition, the analysts need to extract and validate statistical results to facilitate data mining. In this paper, we describe an integrated data mining system called the Linear Correlation Discovery System (LCDS) that meets the above requirement. LCDS consists of four major sub-components, two of which, the selection assistant and the statistics coupler, are discussed in this paper. The former examines the schema and instances to determine appropriate association measurement functions (e.g. chi-square, linear regression, ANOVA). The latter invokes the appropriate statistical function on a sample data set, and extracts relevant statistical output such as ?2, and R2 for effective mining of data. We also describe a new validation algorithm based on measuring the consistency of mining results applied to multiple test sets.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Hawaii International Conference on Systems Sciences (HICSS-33)
City or Country
CHUA, Cecil; CHIANG, Roger Hsiang-Li; and LIM, Ee Peng.
An Integrated Data Mining System to Automate Discovery. (2000). Hawaii International Conference on Systems Sciences (HICSS-33). Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1005
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