Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say that frequent pattern mining was at the cradle of subspace clustering—yet, it quickly developed i...

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The is based on binary(-domain) that are judiciously partially materialized; the missing information can be quickly reconstructed using statistical or linear programming techniques. enables new applications such as exploratory analysis for feature engineering other ...

Résumé. Cet article propose un nouvel algorithme pour le problème de subspace clustering dénommé SNOW. Contrairement aux approches descendantes classiques, il ne repose pas sur l’hypothèse de localité et permet l’affectation d’une donnée à plusieurs clusters dans des sous-espaces différents. Les expérimentations préliminaires montrent que notre approche obtient de meilleurs résultats que l’algo...

Generalized Principal Component Analysis (GPCA): an Algebraic Geometric Approach to Subspace Clustering and Motion Segmentation