Hierarchical and partitioning based hybridized blocking model

Abstract

(Higgins, Savje, & Sekhon, 2016) Provides us with a sampling blocking algorithm that enables large and complex experiments to run in polynomial time without sacrificing the precision of estimates on a covariate dataset. The goal of this project is to run the different clustering algorithms on top of clusters formed from above mentioned blocking algorithm and analyze the performance and compatibility of the clustering algorithms. We first start with applying the blocking algorithm on a covariate dataset and once the clusters are formed, we then apply our clustering algorithm HAC (Hierarchical Agglomerative Clustering) or PAM (Partitioning Around Medoids) on the seeds of the clusters. This will help us to generate more similar clusters. We compare our performance and precision of our hybridized clustering techniques with the pure clustering techniques to identify a suitable hybridized blocking model.