Ensemble Clustering for Boundary Detection in High-Dimensional Data

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.

(Anagnostou, Pavlidis, and Tasoulis 2024)

References

Anagnostou, Panagiotis, Nicos G. Pavlidis, and Sotiris Tasoulis. 2024. “Ensemble Clustering for Boundary Detection in High-Dimensional Data.” In Machine Learning, Optimization, and Data Science, edited by Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos M. Pardalos, and Renato Umeton, 324–33. Cham: Springer Nature Switzerland.