A Generic Methodology for Clustering to Maximises Inter-Cluster Inertia

Abstract

Abstract-This paper proposes a novel clustering methodology which undeniably manages to offer results with a higher inter-cluster inertia for a better clustering. The advantage obtained with this methodology is due to an algorithm that showed beforehand its efficiency in clustering exercises, MC-DBSCAN, which is associated to an iterative process with a potential of auto-adjustment of the weights of the pertinent criteria that allows the reclassification of objects of the two closest clusters through each iteration, as well as the aptitude of the auto-evaluation of the precision of the clustering during the clustering process. This work conducts the experiments using the well-known benchmark, 'Seismic', 'Landform-Identification' and 'Image Segmentation', to compare the performance of the proposed methodology with other algorithms (K-means, EM, CURE and MC-DBSCAN). The experimental results demonstrate that the proposed solution has good quality of clustering results.

Genomic and Transcriptomic Analyses of Malignant Pleural Mesothelioma (MPM) Samples Reveal Crucial Insights for Preclinical Testing

“…Differentially expressed (DE) genes were identified using the R package edgeR (version 3.36.0) [30] from Bioconductor, using a generalized linear model (glm) regression, a quasilikelihood (QL) differential expression test, and the trimmed means of M-values (TMM) normalization. Clustering of results was performed using base R and the inertia method to identify distinct clusters [31].…”