Algorithm to determine ?-distance parameter in density based clustering
Proposed density based clustering approach adaptively determines ?-distance parameter. The methodology is based on the notion of k-nearest neighbours concept. Clustering quality of dimensions depend on the data distribution along that dimension. Dimensions having clustering quality less than threshold are pruned. This makes it appropriate for high dimensional data, as well varying density data.
density based clustering clustering high dimensional data data mining subspace clustering