Mixed data comprises both numeric and categorical features, and mixed datasets occur
frequently in many domains, such as health, finance, and marketing. Clustering is often …

Can we find heterogeneous clusters hidden in data sets with 80% noise? Although such
settings occur in the real-world, we struggle to find methods from the abundance of …

Determining the right code reviewer for a given code change requires understanding the
characteristics of the changed code, identifying the skills of each potential reviewer …

Is there an optimal dimensionality reduction for k-means, revealing the prominent cluster
structure hidden in the data? We propose SUBKMEANS, which extends the classic k-means …

A huge object collection in high-dimensional space can often be clustered in more than one
way, for instance, objects could be clustered by their shape or alternatively by their color …

Finding meaningful clustering patterns in data can be very challenging when the clusters are
of arbitrary shapes, different sizes, or densities, and especially when the data set contains …

Deep clustering techniques combine representation learning with clustering objectives to
improve their performance. Among existing deep clustering techniques, autoencoder-based …

We discuss topological aspects of cluster analysis and show that inferring the topological
structure of a dataset before clustering it can considerably enhance cluster detection: we …

Abstract Large-Scale Subspace Clustering (LSSC) is an interesting and important problem
in big data era. However, most existing methods (ie, sparse or low-rank subspace clustering) …

Over the last decade, the Dip-test of unimodality has gained increasing interest in the data
mining community as it is a parameter-free statistical test that reliably rates the modality in …