Sparse Weighted K-Means for Groups of Mixed-Type Variables
Chavent, Marie; Olteanu, Madalina; Cottrell, Marie; Lacaille, Jérôme; Mourer, Alex (2022), Sparse Weighted K-Means for Groups of Mixed-Type Variables, in Jan Faigl, Madalina Olteanu, Jan Drchal, Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, Springer International Publishing : Berlin Heidelberg, p. 1-10. 10.1007/978-3-031-15444-7_1
TypeCommunication / Conférence
Conference cityBerlin Heidelberg
Book titleAdvances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
Book authorJan Faigl, Madalina Olteanu, Jan Drchal
MetadataShow full item record
Abstract (EN)Assessing the underlying structure of a dataset is often done by training a clustering procedure on the features describing the data. In practice, while the data may be described by a large number of features, only a minority of them may be actually informative with regard to the structure. Furthermore, redundant features may also bias the clustering, whether one speaks of redundancy in the informative or the uninformative features. The present contribution aims at illustrating two sparse clustering methods designed for mixed data (made of numerical and categorical features). The proposed methods summarise redundant features into groups, and select the most relevant groups of features only in the clustering procedure. The performances and the interpretability of the sparse methods are illustrated on a real-life data set.
Subjects / KeywordsSparse clustering; Feature clustering; Feature selection; Group of features selection; Variable importance
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