# MultiKOC: Multi-One-Class Classifier Based K-Means Clustering
## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **\#Clusters** & **MultiKOC(J48)** & **OC-J48** & **Two-J48** \\ \hline SynData & 3 & 0.263 & 0.405 & 0.999 \\ \hline Iris-setosa-versicolor & 3 & 0.325 & 0.000 & 0.960 \\ \hline Iris-setosa-verginica & 3 & 0.303 & 0.000 & 0.956 \\ \hline Iris-versicolor-verginica & 4 & 0.286 & 0.754 & 0.995 \\ \hline newthyroid class 1 & 2 & 0.330 & 0.275 & 0.947 \\ \hline class1\_2\_newthyroid & 2 & 0.447 & 0.052 & 0.980 \\ \hline class1\_3\_newthyroid & 2 & 0.535 & 0.387 & 0.979 \\ \hline class2\_3\_newthyroid & 5 & 0.102 & 0.000 & 0.886 \\ \hline
**Average** & & 0.355 & 0.267 & 0.974 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results summary for J48 classifier. The column #clusters is the optimal number of clusters obtained for MultiKOC. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **\#Clusters** & **MultiKOC(NB)** & **OC-NB** & **Two-NB** \\ \hline SynData & 3 & 0.550 & 0.147 & 0.876 \\ \hline Iris-setosa-versicolor & 3 & 0.529 & 0.597 & 0.929 \\ \hline Iris-setosa-verginica & 3 & 0.443 & 0.271 & 0.937 \\ \hline Iris-versicolor-verginica & 4 & 0.498 & 0.963 & 1.000 \\ \hline newthyroid class 1 & 2 & 0.771 & 0.768 & 0.954 \\ \hline class1\_2\_newthyroid & 2 & 0.816 & 0.854 & 0.986 \\ \hline class1\_3\_newthyroid & 2 & 0.723 & 0.720 & 0.986 \\ \hline class2\_3\_newthyroid & 5 & 0.083 & 0.081 & 0.873 \\ \hline
**Average** & & 0.619 & 0.617 & 0.952 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results summary for Naive Bayes classifier. The column #clusters is the optimal number of clusters obtained for MultiKOC. The third experiment was conducted using the Support Vector Machine classifier, as can be seen in Table 4. The performance of the multiKOC(SVM) outperforms the classic one-class SVM classifier in five experiments out of eight. Moreover, as can be seen in Table 4, the averaged performance of the new proposed method outperforms the classical one by more than 10%. The fourth experiment was conducted using the Random Forest classifier, as can be seen in Table 5. The performance of the multiKOC(RF) was equivalent to the result of the classic one-class classifier. Moreover, we can see that in all the algorithms above, the proposed MultiKOC methods outperform or are comparable to the existing methods. The results are summarized in Table 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **MultiKOC** & **OC** \\ \hline J48 & 0.355 & 0.267 \\ \hline Naive Bayes & 0.619 & 0.617 \\ \hline Support Vector Machine & 0.527 & 0.420 \\ \hline Random Forest & 0.409 & 0.404 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results summary for all the classifiers from the different datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **\#Clusters** & **MultiKOC(SVM)** & **OC-SVM** & **Two-SVM** \\ \hline SynData & 3 & 0.150 & 0.157 & 1.000 \\ \hline Iris-setosa-versicolor & 3 & 0.792 & 0.691 & 0.980 \\ \hline Iris-setosa-verginica & 3 & 0.258 & 0.275 & 0.979 \\ \hline Iris-versicolor-verginica & 4 & 0.963 & 0.682 & 1.000 \\ \hline newthyroid class 1 & 2 & 0.355 & 0.242 & 0.971 \\ \hline class1\_2\_newthyroid & 2 & 0.676 & 0.592 & 0.925 \\ \hline class1\_3\_newthyroid & 2 & 0.498 & 0.302 & 0.932 \\ \hline class2\_3\_newthyroid & 5 & 0.082 & 0.088 & 0.944 \\ \hline
**Average** & & 0.527 & 0.420 & 0.970 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results summary for Support Vector Machine classifier. The column #clusters is the optimal number of clusters obtained for MultiKOC.