Adaptive Clustering for Self-aware Machine Analytics

Clustering is a sub-domain in data mining. To improve the quality of the cluster, adaptive clustering is utilized in external feedback, and experience is utilized to reduce the processing time. In an adaptive clustering environment, the benefit points of sequential data clustering are learned using Q-learning. Adaptive clustering is mainly focused on the reuse of clusters based on previous work. There is a lot of space is available to research in the field of adaptive clustering. The field of adaptive clustering algorithms is mostly untapped, leaving a lot of room for research. Adaptive clustering techniques are effective in circumstances where things change often. Despite shifting environmental conditions and demands, attentive systems are more capable of modifying their efforts, and wealth is identified to achieve a given goal. This potential is the most advantageous to all types of systems in all the domains such as mobile computing, cloud computing, multicore computing, adaptive and dynamic compilation environments, and parallel operating systems, where power, performance, and resource metering challenges must be met. This chapter mainly focused on discussing various types of Ada clustering, Self-Awareness, and Self-Aware decision-making.