# K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data
## 4 Discussion  
Based on the results obtained in this study, Table 4 lists the advantages and disadvantages of each algorithm. K-RBBSO is a good algorithm for big data. The RBBSO algorithm is also effective but has a longer calculation time than K-RBBSO because RBBSO randomly sets the initial solution. Moreover, the K-RBBSO algorithm can overcome the limitations of SA and ABC algorithms; that is, the previous result has a negative effect on the next calculation, and the silhouette cannot be applied in the original search function. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Name** & & **K-RBBSO** & **RBBSO** & **SA** & **ABC** \\ \hline \multirow{4}{*}{Iris} & Average & 2.973 & 2.949 & 12.600 & 3.200 \\  & S.D. & 0.334 & 0.135 & 0.699 & 2.300 \\  & Min & 2.113 & 2.723 & 12.000 & 1.000 \\  & Max & 3.315 & 3.166 & 14.000 & 7.000 \\ \hline \multirow{4}{*}{Wine} & Average & 6.104 & 6.355 & 35.200 & 4.300 \\  & S.D. & 1.701 & 1.940 & 1.317 & 0.483 \\  & Min & 4.376 & 3.895 & 34.000 & 4.000 \\  & Max & 9.090 & 11.156 & 38.000 & 5.000 \\ \hline \multirow{4}{*}{Glass} & Average & 18.755 & 20.193 & 40.500 & 8.200 \\  & S.D. & 6.336 & 8.303 & 1.716 & 1.874 \\  & Min & 9.688 & 9.844 & 38.000 & 5.000 \\  & Max & 28.606 & 35.466 & 43.000 & 11.000 \\ \hline \multirow{4}{*}{Vowel} & Average & 620.514 & 634.797 & 428.900 & N/A \\  & S.D. & 84.979 & 40.503 & 71.086 & N/A \\  & Min & 528.314 & 579.078 & 345.000 & N/A \\  & Max & 770.179 & 714.604 & 508.000 & N/A \\ \hline \multirow{4}{*}{Cloud} & Average & 1592.942 & 1976.802 & 932.600 & N/A \\  & S.D. & 360.610 & 859.481 & 15.771 & N/A \\  & Min & 687.720 & 1422.610 & 902.000 & N/A \\  & Max & 2094.100 & 3775.230 & 951.000 & N/A \\ \hline \multirow{4}{*}{CMC} & Average & 2096.927 & 2932.954 & 1466.800 & N/A \\  & S.D. & 353.531 & 1341.953 & 35.241 & N/A \\ \cline{1-1}  & Min & 1901.350 & 1916.462 & 1419.000 & N/A \\ \cline{1-1}  & Max & 3036.950 & 524.389 & 1534.000 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of calculation time. The relative efficiency of the BBO algorithm designed by [PERSON] and [PERSON] [19] could not be assessed because Euclidean distance is specified as the evaluation function. Thus, it is only applicable to cases with pre-determined numbers of clusters. However, the volume of data is uncertain in arbitrary cases, and the number of clusters must be dynamically decided. In these cases, the silhouette coefficient outperforms the Euclidean distance as the former enables data analysis without prior knowledge of its characteristics. In this study, the aforementioned fact is verified by evaluating the properties of the silhouette coefficient and by improving the computing speed using immigration, mutation, and emigration habitats, which are core components of the BBO algorithm. In addition, K-RBBSO with k-means is proposed to control the randomness involved in the selection of the initial solution. Our results indicate that the algorithm proposed in this study is ineffective only corresponding to a limited amount of data. This is corroborated by the calculated silhouette coefficient. However, in the case of the SA or ABC algorithms, the clustering parameters, as well as the data-related factors, require alteration to yield optimal solutions. Moreover, both algorithms obtained low evaluation values corresponding to Glass, Vowel, Cloud, and CMC data, while the ABC algorithm failed to produce any result at all. The SA algorithm includes a self-termination condition, which decreases the termination time progressively at the maximum temperature, T. In this study, the maximum temperature T condition was removed, and the algorithm was allowed to run until results comparable to those obtained using K-RBBSO were obtained. However, an optimum solution could not be obtained even after 12 h, and the ABC algorithm failed to provide any results whatsoever.