What is it about?
AutoML for supervised learning is widely explored. Contrary to that, automated clustering poses various challenges and remains less explored. In this work we introduce readers to existing works, identify commonalities and differentiating factos, as well as identify the most prominent open research directions and challenges.
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Why is it important?
This survey depicts our effort towards better understanding of this domain and we sincerely hope it will help others in their search or potential development of new solutions. We understand how crucial is for data science practitioners to have tools available that aid with the machine learning design process, ultimately lowering the entry knowledge barrier and providing time efficient approaches.
Perspectives
As developers of our own AutoML clustering framework and researchers on the field, collecting and studying the information presented in this paper was necessary. We hope it will provide the same benefits, while alleviating the time consuming task of manual search, to the reader and potential practitioners.
Yannis Poulakis
Read the Original
This page is a summary of: A Survey on AutoML Methods and Systems for Clustering, ACM Transactions on Knowledge Discovery from Data, February 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3643564.
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