Internal cluster validity measures (such as the Calinski–Harabasz, Dunn, or Davies–Bouldin
indices) are frequently used for selecting the appropriate number of partitions a dataset …

Clustering is an unsupervised learning technique that discovers intrinsic groups based on
proximity between data points. Therefore, the performance of clustering techniques mainly …

Suitable selection of a proximity measure is one of the fundamental requirements of
clustering. With conventional (dis) similarity measures, many clustering algorithms do not …

In this paper, we study finite dimensional vector spaces using rough set theory (RST) by
defining a Boolean information system IB associated with a vector space V for a given basis …

The effectiveness of clustering techniques is significantly influenced by proximity measures
irrespective of type of data and categorical data is no exception. Most of the existing …

Utilizing high-dimensional generalized Fermat points (F d-points) as cluster centers, we
propose a new method F d-points Linkage (FL) for calculating intra-cluster and inter-cluster …

Existing (dis) similarity measures for mixed data often ignore data distribution and ordering
information along numerical and categorical features, respectively, during distance …