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On the Edges of Clustering: Creating Synergies With Related Problems

MCML Authors

Abstract

This thesis explores the connections between clustering and related tasks like subspace clustering, correlation clustering, outlier detection, and data ordering. It introduces novel methods such as the KISS score for subspace clustering, LUCK for correlation clustering, and the ABC algorithm for outlier detection. Additionally, it develops the Circle Index for optimizing data ordering to improve clustering performance. (Shortened.)

phdthesis


Dissertation

LMU München. Nov. 2021

Authors

A. Beer

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: Bee21

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