RelativeDistClust - Clustering with a Novel Non Euclidean Relative Distance
Using the novel Relative Distance to cluster datasets.
Implementation of a clustering approach based on the k-means
algorithm that can be used with any distance. In addition,
implementation of the Hartigan and Wong method to accommodate
alternative distance metrics. Both methods can operate with any
distance measure, provided a suitable method is available to
compute cluster centers under the chosen metric. Additionally,
the k-medoids algorithm is implemented, offering a robust
alternative for clustering without the need of computing
cluster centers under the chosen metric. All three methods are
designed to support Relative distances, Euclidean distances,
and any user-defined distance functions. The Hartigan and Wong
method is described in Hartigan and Wong (1979)
<doi:10.2307/2346830> and an explanation of the k-medoids
algorithm can be found in Reynolds et al (2006)
<doi:10.1007/s10852-005-9022-1>.
