text: 306,79,1470466107672825861,1346905158706483203,Connor Lawless,"Excited to announce that our new #AAAI2022 paper 'Interpretable clustering via Multi-Polytope Machines' is up on arxiv! We present a MINLP formulation for interpretable clustering that jointly clusters points and constructs polytopes around each cluster.  <LINK> The secret sauce in our approach are additional constraints on the hyperplanes in each polytope that allow us to make cluster explanations more interpretable (recovering popular model classes like rule sets and score cards). <LINK> This was work with my amazing (twitter-less) colleagues Lam, Dzung, Jayant, and Chandra at IBM Research from an epic virtual summer internship.",https://arxiv.org/abs/2112.05653,"Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use as a tool for subgroup discovery and description - few state-of-the-art algorithms provide any rationale or description behind the clusters found. We propose a novel approach for interpretable clustering that both clusters data points and constructs polytopes around the discovered clusters to explain them. Our framework allows for additional constraints on the polytopes - including ensuring that the hyperplanes constructing the polytope are axis-parallel or sparse with integer coefficients. We formulate the problem of constructing clusters via polytopes as a Mixed-Integer Non-Linear Program (MINLP). To solve our formulation we propose a two phase approach where we first initialize clusters and polytopes using alternating minimization, and then use coordinate descent to boost clustering performance. We benchmark our approach on a suite of synthetic and real world clustering problems, where our algorithm outperforms state of the art interpretable and non-interpretable clustering algorithms. ",Interpretable Clustering via Multi-Polytope Machines,3,"[""Excited to announce that our new #AAAI2022 paper 'Interpretable clustering via Multi-Polytope Machines' is up on arxiv! We present a MINLP formulation for interpretable clustering that jointly clusters points and constructs polytopes around each cluster. \n\n<LINK>"", 'The secret sauce in our approach are additional constraints on the hyperplanes in each polytope that allow us to make cluster explanations more interpretable (recovering popular model classes like rule sets and score cards). https://t.co/Q0KCUDZtwA', 'This was work with my amazing (twitter-less) colleagues Lam, Dzung, Jayant, and Chandra at IBM Research from an epic virtual summer internship.']",21,12,638