Towards Automated Clustering
Abstract: Automated Machine Learning enables many inexperienced users to generate good classification solutions solely by letting machines generate optimal pipelines. However, there are not as many possibilities to generate meaningful clusterings without further knowledge. As the clustering problem is context and domain dependent, a single solution can never be the sole best clustering for any dataset. To overcome this problem, in this paper we design a framework which uses clustering algorithms, CVIs, the skyline operator and multiple visualization techniques to generate diverse interesting clusterings and present them to a user, enabling him to take an informed decision without needing any experience in the field of clustering.
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