How to Install and Uninstall autoclass Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: April 29,2024

1. Install "autoclass" package

This guide covers the steps necessary to install autoclass on Ubuntu 21.04 (Hirsute Hippo)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                autoclass
            
                    
    
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2. Uninstall "autoclass" package

This guide let you learn how to uninstall autoclass on Ubuntu 21.04 (Hirsute Hippo):


    
        
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                sudo apt remove
            
                        
                autoclass
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the autoclass package on Ubuntu 21.04 (Hirsute Hippo)

Package: autoclass
Architecture: amd64
Version: 3.3.6.dfsg.1-1build1
Priority: optional
Section: universe/math
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian QA Group
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 554
Depends: libc6 (>= 2.14)
Filename: pool/universe/a/autoclass/autoclass_3.3.6.dfsg.1-1build1_amd64.deb
Size: 327744
MD5sum: 50e2296f74d8bb51c92c71bc6579e4e1
SHA1: 620fc1d95f0cb53dbe65e944ed85339de78c4e7d
SHA256: 2302e22add99c398877db2b3975ca60e1c642917d4eb4dc53c723128ac470e78
SHA512: 5711ffbe7274c8bd96101f5aff70c32b44fea8efcc302b42c489b7429a4bff011b56b7cfca4dc92cf70f1e99b6b3efc12ce166122f79a65899324c14e028fa05
Homepage: http://ti.arc.nasa.gov/project/autoclass/
Description-en: automatic classification or clustering
AutoClass solves the problem of automatic discovery of classes in data
(sometimes called clustering, or unsupervised learning), as distinct
from the generation of class descriptions from labeled examples
(called supervised learning). It aims to discover the "natural"
classes in the data. AutoClass is applicable to observations of
things that can be described by a set of attributes, without referring
to other things. The data values corresponding to each attribute are
limited to be either numbers or the elements of a fixed set of
symbols. With numeric data, a measurement error must be provided.
Description-md5: de2c363babb95350ce70fe0aa77aa2d5