Abstract
Clustering is a crucial step in data analysis refining process. Predictive analytics is carried out using a variety of clustering approaches, including partitioning clustering, hierarchical clustering, grid-based clustering, model-based, graph-based clustering, and intensity clustering, among others. The hierarchical approach allows one to cluster data objects into a tree-like structure. The cluster is the name given to each node in the hierarchy. There are two types of hierarchical clustering: agglomerative clustering and divisive clustering. Clustering by agglomeration is often desirable. The performance of the clusters ought to be good for a successful cluster analysis. In this document, we will be using three components to assess cluster reliability: cohesion calculation, silhouette index, and length of time.
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Sridevi, K.N., Pinnapati, S., Prakasha, S. (2022). Hierarchical Cluster-Based Model to Evaluate Accuracy Metrics Based on Cluster Efficiency. In: Nagar, A.K., Jat, D.S., MarÃn-Raventós, G., Mishra, D.K. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 334. Springer, Singapore. https://doi.org/10.1007/978-981-16-6369-7_61
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