Abstract
While explainable AI (XAI) is gaining in popularity, other more traditional machine learning algorithms can also benefit from increased explainability. A semi-supervised approach to correlation clustering opens up a promising design space that might provide such explainability to correlation clustering algorithms. In this work, semi-supervised linear correlation clustering is defined as the task of finding arbitrary oriented subspace clusters using only a small sample of supervised background knowledge provided by a domain experts. This work describes a first foray into this novel approach and provides an implementation of a basic algorithm to perform this task. We have found that even a small amount of supervised background knowledge can significantly improve the quality of correlation clustering in general. With confidence it can be stated, the results of this work have the potential to inspire several more semi-supervised approaches to correlation clustering in the future.
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This work has been funded by the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IS18036A. The authors of this work take full responsibilities for its content.
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Hünemörder, M.A.X., Kazempour, D., Kröger, P., Seidl, T. (2019). SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge. In: Amato, G., Gennaro, C., Oria, V., Radovanović , M. (eds) Similarity Search and Applications. SISAP 2019. Lecture Notes in Computer Science(), vol 11807. Springer, Cham. https://doi.org/10.1007/978-3-030-32047-8_20
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