2023
DOI: 10.3390/fi15100341
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kClusterHub: An AutoML-Driven Tool for Effortless Partition-Based Clustering over Varied Data Types

Konstantinos Gratsos,
Stefanos Ougiaroglou,
Dionisis Margaris

Abstract: Partition-based clustering is widely applied over diverse domains. Researchers and practitioners from various scientific disciplines engage with partition-based algorithms relying on specialized software or programming libraries. Addressing the need to bridge the knowledge gap associated with these tools, this paper introduces kClusterHub, an AutoML-driven web tool that simplifies the execution of partition-based clustering over numerical, categorical and mixed data types, while facilitating the identification… Show more

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Cited by 2 publications
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