From Fig. 14, it X-NeuNAc can be seen that for the incomplete N250C5 data sets at four missing rates, the trend lines of mean prototype error are all the TP-type, whose turning points all appear in the K = 0.01. From Fig. 14(c), we can see that even if the OCS algorithm converges to a wrong solution, it is still able to be guided to the correct solution by the prior distribution information of missing values.
5.4. Experiments on real-world data sets
Besides the synthetic data sets, three real-world data sets, i.e., Crude-Oil, Glass and IRIS [45] and [47], are also used to validate the proposed algorithm. Here, we set six missing rates from 5% to 30%.
5.4.1. Crude-Oil data set
The data set has 56 data points and five attributes, which is phycocyanin a chemical analysis of crude-oil samples from three zones of sandstone. In the data set, 7 samples are from Wilhelm, 11 samples from Sub-Mulinia and 38 samples from Upper-Mulinia. The average experimental results of 10 experiments for the incomplete Crude-Oil data sets are listed in Table 7, and the trend of mean prototype error of the POCS algorithm with the value of K is shown in Fig. 15.
5.4. Experiments on real-world data sets
Besides the synthetic data sets, three real-world data sets, i.e., Crude-Oil, Glass and IRIS [45] and [47], are also used to validate the proposed algorithm. Here, we set six missing rates from 5% to 30%.
5.4.1. Crude-Oil data set
The data set has 56 data points and five attributes, which is phycocyanin a chemical analysis of crude-oil samples from three zones of sandstone. In the data set, 7 samples are from Wilhelm, 11 samples from Sub-Mulinia and 38 samples from Upper-Mulinia. The average experimental results of 10 experiments for the incomplete Crude-Oil data sets are listed in Table 7, and the trend of mean prototype error of the POCS algorithm with the value of K is shown in Fig. 15.