Fast algorithms for single, average/UPGMA and complete linkage clustering.
|Latest on Hackage:||0.4.7|
Module documentation for 0.4.6
This package provides a function to create a dendrogram from a list of items and a distance function between them. Initially a singleton cluster is created for each item, and then new, bigger clusters are created by merging the two clusters with least distance between them. The distance between two clusters is calculated according to the linkage type. The dendrogram represents not only the clusters but also the order on which they were created.
This package has many implementations with different
performance characteristics. There are SLINK and CLINK
algorithm implementations that are optimal in both space and
time. There are also naive implementations using a distance
matrix. Using the
dendrogram function from
Data.Clustering.Hierarchical automatically chooses the best
implementation we have.
Changes in version 0.4:
Specialize the distance type to Double for efficiency reasons. It's uncommon to use distances other than Double.
Implement SLINK and CLINK. These are optimal algorithms in both space and time for single and complete linkage, respectively, running in O(n^2) time and O(n) space.
Reorganized internal implementation.
Some performance improvements for the naive implementation.
Better test coverage. Also, performance improvements for the test suite, now running in 3 seconds (instead of one minute).
Changes in version 0.3.1.2 (version 0.3.1.1 was skipped):
Added tests for many things. Use
Changes in version 0.3.1:
Works with containers 0.4 (thanks, Doug Beardsley).
Removed some internal unnecessary overheads and added some strictness.
Changes in version 0.3.0.1:
Listed changes of unreleased version 0.2.
Changes in version 0.3:
Fixed complexity in Haddock comments.
Changes in version 0.2:
Added separate functions for each linkage type. This may be useful if you want to create a dendrogram and your distance data type isn't an instance of