Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gupta, Shrutika; Naskar, Annesha; Misra, Bitan; Hemanth, K.S.; Chakraborty, Sayan; Kulkarni, Aparna Shrikant Title A name given to the resource Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN;Issue;2025;pp.418-423 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRCICN68210.2025.11364795" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRCICN68210.2025.11364795</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035367086?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035367086?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gupta S., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Naskar A., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Misra B., Techno International New Town, Department of Computer Science and Engineering, West Bengal, India; Hemanth K.S., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Chakraborty S., JIS College of Engineering, Department of Computer Science and Technology, West Bengal, Kalyani, India; Kulkarni A.S., MIT Academy of Education, Alandi, Department of Computer Science and Engineering (Data Science), Pune, India Description An account of the resource To address the demand for exact data removal in unsupervised clustering, a novel framework for exact machine unlearning is proposed that integrates the K-Means++ algorithm with ARCANE. This framework combines high-quality cluster initialization with targeted partitioning, allowing a more efficient method for removing data without the need for a naive retraining of the model. The proposed model is compared to a SISA-based approach against synthetic and Iris datasets. The ARCANE K-Means++ model demonstrated superior clustering quality, achieving a Silhouette Score of 0.841 to the baseline's performance of 0.263. ARCANE framework also demonstrated better speedup and predictable unlearning times for typical deletion requests than the SISA model. This is a strong, scalable, and provably-exact method for machine unlearning, providing a new and intuitive framework for developing privacy-preserving AI. 2025 IEEE. Subject The topic of the resource ARCANE; Clustering; K-means++; Machine Unlearning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 28323645; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online