KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

نویسندگان

چکیده

Abstract Pattern discovery in multidimensional data sets has been the subject of research for decades. There exists a wide spectrum clustering algorithms that can be used this purpose. However, their practical applications share common post-clustering phase, which concerns expert-based interpretation and analysis obtained results. We argue bottleneck process, especially cases where domain knowledge prior to clustering. Such situation requires not only proper automatically discovered clusters but also conformance checking with existing knowledge. In work, we present Knowledge Augmented Clustering ( KnAC ). Its main goal is confront labelling automated sake updating refining former. Our solution restricted any algorithm. Instead, serve as an augmentation arbitrary algorithm, making approach robust model-agnostic improvement state-of-the-art method. demonstrate feasibility our method on artificially, reproducible examples real life use case scenario. both cases, achieved better results than classic without augmentation.

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ژورنال

عنوان ژورنال: Applied Intelligence

سال: 2022

ISSN: ['0924-669X', '1573-7497']

DOI: https://doi.org/10.1007/s10489-022-04310-9