Semi-Supervised Clustering Of Stable Instances.

Date of Submission

December 2018

Date of Award

Winter 12-12-2019

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Electronics and Communication Sciences Unit (ECSU-Kolkata)


Das, Swagatam (ECSU-Kolkata; ISI)

Abstract (Summary of the Work)

Clustering is one of the fundamental problems in data mining. Various objective functions, like k-means, k-median, k-mediod have been applied to solve clustering problems. While all these objectives are NP-Hard in the worst case, practitioners have found remarkable success in applying heuristics like Lloyd’s algorithm for this problem. The following question then becomes important: what properties of real-world instances will enable us to design efficient algorithms and prove guarantees for finding the optimal clustering? We consider the case of multiplicative perturbation stability. Stable instances have an optimal solution that does not change when the distances are perturbed. This captures the notion that optimal solution is tolerant to measurement errors and uncertainty in the points. Semi-supervision allows us to have an oracle O which answers pairwise queries. We design efficient algorithms which solve problems of multiplicative perturbation stability using a noisy oracle model.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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