A-MKMC: An effective adaptive-based multilevel K-means clustering with optimal centroid selection using hybrid heuristic approach for handling the incomplete data

In general, clustering is defined as partitioning similar and dissimilar objects into several groups. It has been widely used in applications like pattern recognition, image processing, and data analysis. When the dataset contains some missing data or value, it is termed incomplete data. In such implications, the incomplete dataset issue is untreatable while validating the data. Due to these flaws, the quality or standard level of the data gets an impact. Hence, the handling of missing values is done by influencing the clustering mechanisms for sorting out the missing data. Yet, the traditional clustering algorithms fail to combat the issues as it is not supposed to maintain large dimensional data. It is also caused by errors of human intervention or inaccurate outcomes. To alleviate the challenging issue of incomplete data, a novel clustering algorithm is proposed. Initially, incomplete or mixed data is garnered from the five different standard data sources. Once the data is to be collected, it is undergone the pre-processing phase, which is accomplished using data normalization. Subsequently, the final step is processed by the new clustering algorithm that is termed Adaptive centroid based Multilevel K-Means Clustering (A-MKMC), in which the cluster centroid is optimized by integrating the two conventional algorithms such as Border Collie Optimization (BCO) and Whale Optimization Algorithm (WOA) named as Hybrid Border Collie Whale Optimization (HBCWO). Therefore, the validation of the novel clustering model is estimated using various measures and compared against traditional mechanisms. From the overall result analysis, the accuracy and precision of the designed HBCWO-A-MKMC method attain 93 % and 95 %. Hence, the adaptive clustering process exploits the higher performance that aids in sorting out the missing data issuecompared to the other conventional methods.