How to Install and Uninstall libghc-hierarchical-clustering-doc Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 12,2024

1. Install "libghc-hierarchical-clustering-doc" package

Here is a brief guide to show you how to install libghc-hierarchical-clustering-doc on Ubuntu 20.10 (Groovy Gorilla)

$ sudo apt update $ sudo apt install libghc-hierarchical-clustering-doc

2. Uninstall "libghc-hierarchical-clustering-doc" package

Please follow the guidelines below to uninstall libghc-hierarchical-clustering-doc on Ubuntu 20.10 (Groovy Gorilla):

$ sudo apt remove libghc-hierarchical-clustering-doc $ sudo apt autoclean && sudo apt autoremove

3. Information about the libghc-hierarchical-clustering-doc package on Ubuntu 20.10 (Groovy Gorilla)

Package: libghc-hierarchical-clustering-doc
Architecture: all
Version: 0.4.7-1build1
Priority: extra
Section: universe/doc
Source: haskell-hierarchical-clustering
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Haskell Group
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 633
Depends: haddock-interface-35
Recommends: libjs-mathjax
Filename: pool/universe/h/haskell-hierarchical-clustering/libghc-hierarchical-clustering-doc_0.4.7-1build1_all.deb
Size: 53864
MD5sum: ef56464b953650604eabf2fe9e5a2fa1
SHA1: 7134e384c1a67d2fdf4c69f7699b7e138ab9b89d
SHA256: 2b89e3ed24aed76620e7f7f40dc45078b3879d63d45362575e2f6e49fcca510e
SHA512: 411ba634bf8a9b8742aac1c73936be8d1622cdb9bc6c876e670eddd93076f0338c0ee53b579f5981d3fa2c5fa53a5876178cd8938653dc394932fbb799031143
Homepage: https://hackage.haskell.org/package/hierarchical-clustering
Description-en: fast algorithms for single, average/UPGMA and complete linkage clustering; documentation
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.
.
This package provides the documentation for a library for the Haskell
programming language.
See http://www.haskell.org/ for more information on Haskell.
Description-md5: 525776fdf18583782ab432bc474bceaa