Automobile Data Set
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Geraldine E. Rosario and Elke A. Rundensteiner and David C. Brown and Matthew O. Ward. Mapping Nominal Values to Numbers for Effective Visualization. INFOVIS. 2003.
strength of association between two nominal variables)? In general, which quantification do you feel is better (easier to understand, more believable ordering and spacing)? 7.5.1 Automobile Data Set Case Study We chose the Automobile Data Set because it is easy to interpret. The variables we analyzed are make, fuel type, aspiration, number of doors, body type, wheels, engine location, engine
Wai Lam and Kin Keung and Charles X. Ling. PR 1527. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. 2001.
find that the abstraction method reduces the average data retention rate of RT3 from 14.2% to 6.6% with a 43 UNCORRECTED PROOF PR1527 W.Lam et al./ Pattern Recognition 000 (2001) 000--000 9 Table 1 Data sets and their codes Data set Code Automobile Ab Auto-Mpg Am Audiology Au Balance-scale Ba Breast-cancer-w Bc Car Ca Credit screening Cs Ecoli Ec Glass1 Gl Hepati He Ionosphere Io Iris Ir Letter Le
Yongge Wang. A New Approach to Fitting Linear Models in High Dimensional Spaces. Alastair Scott (Department of Statistics, University of Auckland).
together with the number of observations # and the number of variables D in each dataset. The datasets Autos Automobile , Cpu (Computer Hardware), and Cleveland (Heart Disease---Processed Cleveland) 141 Autos Bankbill Bodyfat Cholesterol Cleveland Cpu n / k 159 / 16 71 / 16 252 / 15