How to Install and Uninstall autoclass Package on Ubuntu 21.10 (Impish Indri)

Last updated: April 29,2024

1. Install "autoclass" package

This guide covers the steps necessary to install autoclass on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                autoclass
            
                    
    
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2. Uninstall "autoclass" package

Please follow the step by step instructions below to uninstall autoclass on Ubuntu 21.10 (Impish Indri):


    
        
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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 21.10 (Impish Indri)

Package: autoclass
Architecture: amd64
Version: 3.3.6.dfsg.1-2
Priority: optional
Section: universe/math
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian QA Group
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 562
Depends: libc6 (>= 2.29)
Filename: pool/universe/a/autoclass/autoclass_3.3.6.dfsg.1-2_amd64.deb
Size: 327900
MD5sum: 31c1ae5c91531c0b3848dab2fbd6fa55
SHA1: 9b123c51069a601546ec2e8f22c88a057d12b4d0
SHA256: 137c3245b155084c842741db5970e1b320a089a099fbd1c2b24d3a5a6514a527
SHA512: 83f8856a0997730169a4570a21ca877d68357ed562c4079778e8add5ba3a10eb40dd2d29381c831fac4485a665d3c382125e476c482c3577ec747e5194067caf
Homepage: http://ti.arc.nasa.gov/tech/rse/synthesis-projects-applications/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