The paper "Mixed Data Clustering Survey and Challenges" discusses how the rise of big data has made clustering of heterogeneous datasets—containing both numerical and categorical variables—particularly difficult for traditional methods. It highlights the importance of hierarchical and explainable algorithms for producing interpretable results that aid decision‑making. The authors propose a new clustering approach based on pretopological spaces and benchmark it against classical numerical clustering algorithms and existing pretopological methods to evaluate its performance in the big data context.
By Maxence Choufa, Clement Cornet, Guillaume Guerard, Sonia Djebali, Loup-No\'e Levy
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By Wei Liu, Eric Krokos, Kirsten Whitley, Rebecca Faust, Chris North
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