Ensemble Clustering for Boundary Detection in High-Dimensional Data

Anagnostou, Panagiotis and Pavlidis, Nicos G. and Tasoulis, Sotiris (2024) Ensemble Clustering for Boundary Detection in High-Dimensional Data. In: Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Revised Selected Papers :. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer, GBR, pp. 324-333. ISBN 9783031539657

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Abstract

The emergence of novel data collection methods has led to the accumulation of vast amounts of unlabelled data. Discovering well separated groups of data samples through clustering is a critical but challenging task. In recent years various techniques to detect isolated and boundary points have been developed. In this work, we propose a clustering methodology that enables us to discover boundary data effectively, discriminating them from outliers. The proposed methodology utilizes a well established density based clustering method designed for high dimensional data, to develop a new ensemble scheme. The experimental results demonstrate very good performance, indicating that the approach has the potential to be used in diverse domains.