Abstract
Hierarchical Clustering is an unsupervised learning task, whi-ch seeks to build a set of clusters ordered by the inclusion relation. It is usually assumed that the result is a tree-like structure with no overlapping clusters, i.e., where clusters are either disjoint or nested. In Hierarchical Conceptual Clustering (HCC), each cluster is provided with a conceptual description which belongs to a predefined set called the pattern language. Depending on the application domain, the elements in the pattern language can be of different nature: logical formulas, graphs, tests on the attributes, etc. In this paper, we tackle the issue of overlapping concepts in the agglomerative approach of HCC. We provide a formal characterization of pattern languages that ensures a result without overlaps. Unfortunately, this characterization excludes many pattern languages which may be relevant for agglomerative HCC. Then, we propose two variants of the basic agglomerative HCC approach. Both of them guarantee a result without overlaps; the second one refines the given pattern language so that any two disjoint clusters have mutually exclusive descriptions.
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Notes
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Available at UC Irvine Machine Learning Database http://archive.ics.uci.edu/ml/.
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This research was supported by the QAPE company under the research project Kover.
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Brabant, Q., Mouakher, A., Bertaux, A. (2020). Preventing Overlaps in Agglomerative Hierarchical Conceptual Clustering. In: Alam, M., Braun, T., Yun, B. (eds) Ontologies and Concepts in Mind and Machine. ICCS 2020. Lecture Notes in Computer Science(), vol 12277. Springer, Cham. https://doi.org/10.1007/978-3-030-57855-8_6
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