Using Bagging to improve clustering methods in the context of three-dimensional shapes

Abstract

Cluster Analysis techniques are a common approach to classifying objects within a dataset into distinct clusters. The clustering of geometric shapes of objects holds significant importance in various fields of study. To analyze the geometric shapes of objects, researchers often employ Statistical Shape Analysis methods, which retain crucial information after accounting for scaling, locating, and rotating an object. Consequently, several researchers have focused on adapting clustering algorithms for shape analysis. Recently, three-dimensional (3D) shape clustering has become crucial for analyzing, interpreting, and effectively utilizing 3D data across diverse industries, including medicine, robotics, civil engineering, and paleontology. In this study, we adapt the K-means, CLARANS and Hill Climbing methods using an approach based on the Bagging procedure to achieve enhanced clustering accuracy. We conduct simulation experiments for both isotropy and anisotropy scenarios, considering various dispersion variations. Furthermore, we apply the proposed approach to real datasets from relevant literature. We evaluate the obtained clusters using cluster validation measures, specifically the Rand Index and the Fowlkes-Mallows Index. Our results demonstrate substantial improvements in clustering quality when implementing the Bagging approach in conjunction with the K-means, CLARANS and Hill Climbing methods. The combination of the Bagging method and clustering algorithms provided substantial gains in the quality of the clusters.