How to Install and Uninstall python311-cluster Package on openSuSE Tumbleweed

Last updated: November 23,2024

1. Install "python311-cluster" package

Please follow the guidelines below to install python311-cluster on openSuSE Tumbleweed


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python311-cluster
            
                    
    
    Copied

2. Uninstall "python311-cluster" package

Please follow the guidance below to uninstall python311-cluster on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python311-cluster
            
                    
    
    Copied

3. Information about the python311-cluster package on openSuSE Tumbleweed

Information for package python311-cluster:
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Repository : openSUSE-Tumbleweed-Oss
Name : python311-cluster
Version : 1.4.1.post2-3.2
Arch : noarch
Vendor : openSUSE
Installed Size : 119.6 KiB
Installed : No
Status : not installed
Source package : python-cluster-1.4.1.post2-3.2.src
Upstream URL : https://github.com/exhuma/python-cluster
Summary : Clustering library for python
Description :
The python-cluster package allows you to create several groups
(clusters) of objects from a list. It’s meant to be flexible and able
to cluster any object. To ensure this kind of flexibility, you need
not only to supply the list of objects, but also a function that
calculates the similarity between two of those objects. For simple
datatypes, like integers, this can be as simple as a subtraction, but
more complex calculations are possible. Right now, it is possible to
generate the clusters using a hierarchical clustering and the popular
K-Means algorithm. For the hierarchical algorithm there are different
“linkage” (single, complete, average and uclus) methods available.