A Novel Approach for Efficient Clustering Using Predefined Clusters and Hierarchical Clustering (PECHC)

Abstract

Clustering techniques play a pivotal role in data analysis, facilitating the exploration and organization of complex datasets into meaningful groups. This study proposes a novel approach to the predefined clusters and hierarchical clustering (PECHC) approach, which is intended for efficient clustering using predefined clusters. The primary objectives include enhancing cluster quality balanced with efficiency. The study includes significant parameters that involve the number of iterations, Silhouette Score, and Davies-Bouldin Index to assess PECHC's effectiveness across several datasets. The results of the analysis indicate that PECHC consistently achieved superior clustering performance and efficiency relative to other methods. In terms of methodology, PECHC uses recursive plotting for initial cluster estimation and principal component analysis (PCA) for dimensionality reduction. PECHC shows high cluster separation, competitive clustering accuracy, and efficient convergence on a variety of datasets.