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optpart

Optimal Partitioning of Similarity Relations

Contains a set of algorithms for creating partitions and coverings of objects largely based on operations on (dis)similarity relations (or matrices). There are several iterative re-assignment algorithms optimizing different goodness-of-clustering criteria. In addition, there are covering algorithms 'clique' which derives maximal cliques, and 'maxpact' which creates a covering of maximally compact sets. Graphical analyses and conversion routines are also included.

Functions (38)

Archipelago Analysis

Identify the Goodness-of-Fit of Cluster Members

Best Of Set Optimal Partitions From Random Starts

Classification Matching and Differencing

Maximal Clique Analysis

clique.test

Clique Test

Clustering Object

Compare Species Constancy for Specified Clusters

(Fuzzy) Confusion Matrix

Recommendations for Possible Merging of Clusters

Dissimilarity Diameters of a Partition

Extract A Specific Cluster Solution From A Stride

Calculate a Flexible-Beta Dendrogram

gensilwidth

Generalized Silhouette Width

Goodman- Kruskal Lambda Index of Classification Association

Maximally Similar Sets Analysis

Merge Specified Clusters in a Classification

Indicator Species Analysis by Murdoch Preference Function

Neighbor Analysis of Partitions

Optimum Classification by Counts of Indicator Species

Optimizing Classification by Maximizing Dufrene and Legendre's Indicator Value

Optimal Partitioning of Dissimilarity/Distance Matrices

optpart.internal

Internal Optpart Functions

Clustering by Optimizing Silhouette Widths

Optimizing Classification by Minimizing Table Deviance

Partition Analysis

Convert Object to Partition Object

phi

Calculating the phi Statistic on Taxon Classifications

Refining a Classification by Re-Assigning Memberships

Re-order Clusters in a Classification

Site Data for the Shoshone National Forest, Wyoming, USA

Vascular Plant Species Cover for the Shoshone National Forest, Wyoming, USA

Produce a Silhouette Object From a Partana, Clustering, or Stride Object

Slice a Hierarchical Clustering Dendrogram with a Mouse

Stride: Producing a Sequence of Clusterings

Classification Validity Assessment by Table Deviance

Identify Misclassified Plots in a Partition

Identification of Typal Samples in a Partition

optpart

Optimal Partitioning of Similarity Relations

v3.0-3

GPL (>= 2)

Authors

David W. Roberts <droberts@montana.edu>

Initial release

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