Description:

Affinity propagation (AP) is a clustering algorithm that has been introduced by Brendan J. Frey and Delbert Dueck. The authors themselves describe affinity propagation as follows:

"An algorithm that identifies exemplars among data points and forms clusters of data points around these exemplars. It operates by simultaneously considering all data point as potential exemplars and exchanging messages between data points until a good set of exemplars and clusters emerges."

AP has been applied in various fields recently, among which bioinformatics is becoming increasingly important. Frey and Dueck have made their algorithm available as Matlab code. Matlab, however, is relatively uncommon in bioinformatics. Instead, the statistical computing platform R has become a widely accepted standard in this field. In order to leverage affinity propagation for bioinformatics applications, we have implemented affinity propagation as an R package. Note, however, that the given package is in no way restricted to bioinformatics applications. It is as generally applicable as Frey’s and Dueck’s original Matlab code.

The package further implements leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

Changes to previous version:

• added Leveraged Affinity Propagation Clustering
• re-implementation of main functions as S4 generic methods in order to facilitate the convenient internal computation of similarity matrices
• for convenience, similarity matrices can be stored as part of clustering results
• heatmap plotting now done by heatmap() which has been defined as S4 generic
• extended interface to functions for computing similarity matrices
• added function corSimMat()
• implementation of length() method for classes APResult, AggExResult, and ExClust
• added accessor function to extract clustering levels from AggExResult objects
• correction of exemplars returned by apcluster() for details=TRUE in slot idxAll of returned APResult object
• when using data stored in a data frame, now categorical columns are explicitly omitted, thereby, avoiding warnings
• all clustering methods now store their calls into the result objects
• updates and extensions of help pages and vignette

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Any Format Supported By R

Tags: Clustering, Kernels, Distance Function, Clustering Algorithm, Affinity Propagation, Similarity Measure

Archive: download here

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