Sven Lieber<p>Hey <a href="https://hcommons.social/tags/library" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>library</span></a> folks 👋 ,</p><p>do you want to cluster your book editions with the well-known Work-set algorithm from <a href="https://hcommons.social/tags/OCLC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>OCLC</span></a>, but you don't find a suitable reusable tool?</p><p>I recently faced this issue while working on the <a href="https://hcommons.social/tags/BELTRANS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BELTRANS</span></a> project at KBR (Royal Library of Belgium). All I found were many research papers describing the clustering and a few implementations that required me to install 2010-style Java software stacks.</p><p>So I decided to write an easily reusable small <a href="https://hcommons.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> script that follows the ideas of the Work-set algorithm: clustering based on descriptive keys. Nothing more, nothing less.</p><p>Check my blog post for more information and have a look at the script.</p><p>➡️ blog post: <a href="https://doi.org/10.59350/4hd4r-1tk44" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.59350/4hd4r-1tk44</span><span class="invisible"></span></a></p><p>➡️ script: <a href="https://doi.org/10.5281/zenodo.10011416" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.5281/zenodo.1001141</span><span class="invisible">6</span></a></p><p><a href="https://hcommons.social/tags/FRBRization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>FRBRization</span></a> <a href="https://hcommons.social/tags/FRBR" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>FRBR</span></a> <a href="https://hcommons.social/tags/IFLA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>IFLA</span></a> <a href="https://hcommons.social/tags/clustering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>clustering</span></a></p>