ItemConference Paper

Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning

Title
Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning
Creator
Gupta, Shrutika; Naskar, Annesha; Misra, Bitan; Hemanth, K.S.; Chakraborty, Sayan; Kulkarni, Aparna Shrikant
Description
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.
Source
Proceedings of the International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN;Issue;2025;pp.418-423
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
ARCANE; Clustering; K-means++; Machine Unlearning
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 28323645;
Format
online
Language
English
Type
Conference paper

Citation

Gupta, Shrutika; Naskar, Annesha; Misra, Bitan; Hemanth, K.S.; Chakraborty, Sayan; Kulkarni, Aparna Shrikant, “Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26092.

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