fuzzyclara

Efficient and fuzzy clustering based on the CLARA algorithm

• Authors: Maximilian Weigert, Alexander Bauer, Jana Gauss
• Contributors: Theresa Kriecherbauer, Asmik Nalmpatian
• Version: 1.0.0

Aim of this Package

The fuzzyclara package tackles two issues of cluster analysis applications. First, it includes routines for fuzzy clustering which avoid the common hard clustering assumption that each observation is a clear member of one sole cluster. Instead, membership probabilities indicate to which extent the characteristics of each observation are shaped by the characteristics of several 'typical' clusters. Second, the estimation of classical clustering algorithms is often only hardly or not at all feasible in large data situations with thousands of observations. Subsampling-based algorithms building on the CLARA algorithm are implemented to make the estimation feasible in such situations. Building on these two points, the 'fuzzyclara' package offers routines for all aspects of a cluster analysis, including the use of user-defined distance functions and diverse visualization techniques.

Documentation and Useful Materials

To get an overview of the functionalities of the package, check out the package vignette.

Installation

The most current version from GitHub can be installed via

devtools::install_github("MaxWeigert/fuzzyclara")

How to Contribute

If you encounter problems with the package, find bugs or have suggestions for additional functionalities please open a GitHub issue. Alternatively, feel free to contact us directly via email.

Contributions (via pull requests or otherwise) are welcome. Before you open a pull request or share your updates with us, please make sure that all unit tests pass without errors or warning messages. You can run the unit tests by calling

devtools::test()