Spectral Clustering of Categorical and Mixed-type Data via Extra Graph Nodes

arXiv:2403.05669v1 Announce Type: cross
Abstract: Clustering data objects into homogeneous groups is one of the most important tasks in data mining. Spectral clustering is arguably one of the most important algorithms for clustering, as it is appealing for its theoretical soundness and is adaptable to many real-world data settings. For example, mixed data, where the data is composed of numerical and categorical features, is typically handled via numerical discretization, dummy coding, or similarity computation that takes into account both data …

abstract algorithms arxiv categorical clustering cs.lg data data mining extra graph mining mixed nodes objects stat.ml tasks type via world