5.1.3. Wine data set
The Wine data set consists of 178 thirteen-dimensional attribute vectors that describe the results of a chemical analysis of wines grown in the same region but derived from three different cultivars. The corresponding Obatoclax contain 59 samples, 71 samples, and 48 samples, respectively. We still perform 10 experiments on 10 incomplete Wine data sets, and the average experimental results are shown in Table 3 and Fig. 7.
Table 3.
Averaged results of 10 experiments using incomplete Wine data sets.WDSPDSNPSOCSNNMPOCSMean prototype error19.07800.05210.04670.04250.04150.0398Mean number of misclassifications9.19.19.19.19.19.1Mean imputation error–
–
1.40671.24111.19181.2165Mean number of iterations to termination26.227.127.427.427.227.4Full-size tableTable optionsView in workspaceDownload as CSV
Fig. 7. Trend line of mean prototype error with the epinephrine increase of K for incomplete Wine data sets.Figure optionsDownload full-size imageDownload as PowerPoint slide
The Wine data set consists of 178 thirteen-dimensional attribute vectors that describe the results of a chemical analysis of wines grown in the same region but derived from three different cultivars. The corresponding Obatoclax contain 59 samples, 71 samples, and 48 samples, respectively. We still perform 10 experiments on 10 incomplete Wine data sets, and the average experimental results are shown in Table 3 and Fig. 7.
Table 3.
Averaged results of 10 experiments using incomplete Wine data sets.WDSPDSNPSOCSNNMPOCSMean prototype error19.07800.05210.04670.04250.04150.0398Mean number of misclassifications9.19.19.19.19.19.1Mean imputation error–
–
1.40671.24111.19181.2165Mean number of iterations to termination26.227.127.427.427.227.4Full-size tableTable optionsView in workspaceDownload as CSV
Fig. 7. Trend line of mean prototype error with the epinephrine increase of K for incomplete Wine data sets.Figure optionsDownload full-size imageDownload as PowerPoint slide