# K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data
## 5 Conclusions  
This study focuses on several problems in data clustering--existing formulae require appropriate alterations to make them suitable for the application of the silhouette coefficient; the number of computations required to calculate the silhouette coefficient is very high, and random selection of the initial solution exerts a negative effect on the search for the optimal solution. To overcome these issues, we proposed the K-RBBSO algorithm that uses the silhouette coefficient instead of the Euclidean distance evaluation function, which can only be utilized when the clustering parameters are pre-determined. During the implementation step, the proposed method identifies better solutions based on the silhouette coefficient of each habitat. K-Means is used in this case to reduce the time required to configure the initial solution.  
The results of this study can be applied in the following ways. The proposed method can be utilized by users who are contemplating employing clustering to provide personalized services. With the growing ubiquity of the Internet of Things (IoT), it is now possible to safeguard data regarding individuals. Further, a specialized analytical approach for the present generation is necessary for their unique characteristics. However, altering prior classification/evaluation criteria to the present generation is expected to impact the accuracy.  
Therefore, clustering is necessary to simultaneously examine large amounts of data [22]. Thus, users who must supply tailored products/services to clients by categorizing them based on customer characteristics can find the conclusions drawn in this study useful. The optimization algorithm follows the principle of the BBO algorithm to \"identify better results by gathering good results.\" As a result, any attempt made to develop an algorithm with the goal of \"gathering good solutions to produce better solutions\" to tackle local solution issues, such as the BBO algorithm, can be expected to benefit from the conclusions of this study.  
Future research should primarily focus on determining the optimal number of habitats. K-RBBSO requires fewer types of parameters to be specified compared to other data clustering algorithms. However, the number of habitats is the most critical parameter--it determines both the execution time and the time required to identify an optimum solution.  
Additionally, it is essential to develop a method to reduce the distance calculation process for all data. This is a long-standing problem with the silhouette coefficient. [PERSON]'s [12] silhouette coefficient is effective at identifying more optimum solutions irrespective of the number of clusters. However, because of the evaluation function's characteristics, the distances between all data points must be computed to estimate the relative distance. In future research, a technique to determine relative distances without explicitly measuring the distances between all pairs of data points should be developed.  
Conceptualization: S.P.; formal analysis: S.P.; funding acquisition: S.K.; methodology: S.P.; writing--original draft: S.P.; writing--review and editing: S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the National Research Foundation of Korea and funded by the Korean Government (Ministry of Science and ICT; #2020R1A2B501001801). This research received support from the Ministry of Science and ICT, Korea, under the Information Technology Research Center support program (IITP-2021-0-02051) supervised by the Institute for Information & Communications Technology Planning & Evaluation.  
Not applicable.  
The authors declare no conflict of interest.