clustDDist: Clustering Discrete Distributions
Clustering of units described with distributions is considered. Frequent approach for clustering such data combines non-hierarchical method (to allow clustering of large amount of units) with hierarchical clustering method (to build dendrogram from the obtained nonhierarchical clusters and determine the most 'natural' final clustering(s) from it). The use of the squared Euclidean distance as an error function favors patterns of distributions that have one steep high peak. Here several alternative error functions are implemented. They characterize errors between clustered units and a cluster representative - leader (which needs not be defined in the same space). For these error functions the adapted leaders methods and compatible agglomerative hierarchical clustering methods are implemented.
- Natasa Kejzar, Vladimir Batagelj, Simona Korenjak-Cerne
- Date of publication
- Natasa Kejzar <email@example.com>
- Internal Functions in the clustDDist package
- Clustering (Discrete) Distributions
- Compute Order for Units where Hierarchical Clustering was...
- Returns List to Help Chaining Leaders and Hierarchical...
- Clustering of Units with Adapted Hierarchical Clustering...
- Clustering of Units with Adapted Leaders Method for Different...
- Dataset of US patents from 1980 to 1999, citations...
Files in this package