Analyzing the Use of Cluster Analysis in Identifying Patterns and Segmenting Large Multivariate Datasets

Posted: Dec 07, 2021

• Ella Anderson
• Ella Walker
• Emily Campbell

Abstract

Cluster analysis represents a fundamental methodology in the exploration and understanding of complex multivariate datasets, serving as a cornerstone technique in data mining, pattern recognition, and knowledge discovery. The proliferation of high-dimensional data across scientific, commercial, and social domains has created unprecedented opportunities for extracting meaningful insights through clustering techniques. However, traditional clustering approaches face significant challenges when applied to contemporary datasets characterized by massive scale, high dimensionality, and complex internal structures. This research addresses these challenges through the development and evaluation of novel clustering methodologies that integrate principles from quantum computing with established clustering algorithms. The exponential growth in data collection capabilities has transformed the landscape of data analysis, with multivariate datasets now routinely containing thousands of dimensions and millions of observations. In such environments, conventional clustering techniques often struggle with computational efficiency, sensitivity to initialization parameters, and the curse of dimensionality. This study proposes a quantum-inspired optimization framework that enhances traditional clustering algorithms, particularly focusing on k-means clustering, by incorporating quantum annealing principles for improved centroid initialization and convergence properties. Our research questions center on three primary objectives: first, to quantify the performance improvements achievable through quantum-enhanced clustering methodologies; second, to develop robust validation metrics specifically designed for high-dimensional clustering scenarios; and third, to establish practical guidelines for implementing these advanced clustering techniques across diverse application domains. The novelty of this work lies in its cross-disciplinary approach, bridging quantum computing concepts with practical data analysis needs, while maintaining computational feasibility for real-world applications. This paper makes several distinct contributions to the field of cluster analysis. We introduce a quantum-inspired initialization protocol that significantly