u.HARDCL

x.test

Integer: Number of clusters to get

String: Distance measure to use: 'euclidean' or 'manhattan'

dist

Logical: If TRUE, print messages to screen

verbose

Additional parameters to be passed to <code>flexclust::cclust</code>

Perform clustering by Hard Competitive Learning using <code>flexclust::cclust</code>

Advanced Machine Learning and Visualization made efficient and accessible. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression, Survival Analysis), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and dynamic graphs.

u.HARDCL: Clustering by Hard Competitive Learning

Description

Usage

Arguments

See Also