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Parameter Free Clustering with Cluster Catch Digraphs (Technical Report)
CCDs are appealing digraphs for clustering, since they estimate the number of clusters; however, CCDs (and density-based methods in general) require some information on a parameter representing the \emph{intensity} of assumed clusters in the data set.
Classification of Imbalanced Data with a Geometric Digraph Family
We use a geometric digraph family called class cover catch digraphs (CCCDs) to tackle the class imbalance problem in statistical classification.
Classification Using Proximity Catch Digraphs (Technical Report)
We also show that, similar to CCCD classifiers, our classifiers are relatively better in classification in the presence of class imbalance.
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