How to Install and Uninstall python38-cluster Package on openSuSE Tumbleweed
Last updated: November 05,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python38-cluster" package
This guide let you learn how to install python38-cluster on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python38-cluster
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2. Uninstall "python38-cluster" package
Here is a brief guide to show you how to uninstall python38-cluster on openSuSE Tumbleweed:
$
sudo zypper remove
python38-cluster
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3. Information about the python38-cluster package on openSuSE Tumbleweed
Information for package python38-cluster:
-----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-cluster
Version : 1.4.1.post2-2.2
Arch : noarch
Vendor : openSUSE
Installed Size : 128,8 KiB
Installed : No
Status : not installed
Source package : python-cluster-1.4.1.post2-2.2.src
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.
-----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-cluster
Version : 1.4.1.post2-2.2
Arch : noarch
Vendor : openSUSE
Installed Size : 128,8 KiB
Installed : No
Status : not installed
Source package : python-cluster-1.4.1.post2-2.2.src
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.