Scott, P.D. and Coxon, A.P.M. and Hobbs, M.H.W. and Williams, R.J. (1997) An intelligent assistant for exploratory data analysis. In: Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science, 1263 (1263). Springer Verlag pp. 189-199. ISBN 3-540-63223-9.
In this paper we present an account of the main features of SNOUT, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships ina data set rather than simply develop predictive models for selectd variables. Brief descriptions of a number of novel techniques developed for use in SNOUT are presented. These include heuristic variable level inference and classification, automatic category formation, the use of similarity trees to identify groups of related variables, interactive decision tree construction and model selection using a genetic algorithm.
|Item Type:||Conference or workshop item (Paper)|
|Uncontrolled keywords:||data mining, exploratory data analysis, decision tree clasifier, genetic algorithm|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing|
|Depositing User:||Mark Wheadon|
|Date Deposited:||25 Aug 2009 17:20|
|Last Modified:||25 Jun 2012 10:47|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/21552 (The current URI for this page, for reference purposes)|
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