Conventional Clustering

Conventional clustering aims to group similar data points into clusters, revealing underlying structure in datasets. Current research focuses on improving the efficiency and accuracy of existing algorithms like K-means and spectral clustering, addressing challenges such as handling mixed-data types, high dimensionality, and the need for label-free or supervised approaches. These advancements are impacting diverse fields, enabling better analysis of complex data in areas like social network analysis, bioinformatics, and recommendation systems. New methods leverage information theory, distance distributions, and even deep learning to enhance clustering performance and interpretability.