Evaluation of Clustering Patterns using Singular Value Decomposition (SVD)

Abstract

Computational techniques, such as Simple K, have been used for exploratory analysis in applications ranging from data mining research, machine learning, and computational biology. The medical domain has benefitted from these applications, and in this regard, the authors analyze patterns in individuals of selected age groups linked with the possibility of Metabolic Syndrome (MetS), a disorder affecting approximately 45% of the elderly. The study identifies groups of individuals behaving in two defined categories, that is, those diagnosed with MetS (MetS Positive) and those who are not (MetS Negative), comparing the pattern definition. The paper compares the cluster formation in patterns when using a data reduction technique referred to as Singular Value Decomposition (SVD) versus eliminating its application in clustering. Data reduction techniques like SVD have proved to be very useful in projecting only what is considered to be key relations in the data by suppressing the less important ones. With the existence of high dimensionality, the importance of SVD can be highly effective. By applying two internal measures to validate the cluster quality, findings in this study prove interesting in context to both approaches.