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...

A completely general method of analysis for three-dimensional raked piles under harmonic excitation is discussed. The piles have been represented by a three-dimensional frame structure and the soil has been represented by a boundary element discretization scheme. A computer program has been written which carries out this analysis and produces a group stiffness matrix that can be included as a f...

While subspace clustering emerged as an application of pattern mining and some of its early advances have probably been inspired by developments in pattern mining, over the years both fields progressed rather independently. In this paper, we identify a number of recent developments in pattern mining that are likely to be applicable to alleviate or solve current problems in subspace clustering a...

The curse of dimensionality turns the high-dimensional data analysis a challenging task for data clustering techniques. In order to deal with highdimensional data, this paper presents a clustering approach that explores the combination of two strategies: semi-supervision and density estimation based on hubness scores. Initial experimental results show a good performance when applied on real dat...

Ant-based clustering is a nature-inspired technique whereas stochastic agents perform the task of clustering high-dimensional data. This paper analyzes the popular technique of Lumer/Faieta. It is shown that the Lumer/Faieta approach is strongly related to Kohonen’s SelfOrganizing Batch Map. A unifying basis is derived in order to assess strengths and weaknesses of both techniques. The behaviou...

Data mining is a process of discovering and exploiting hidden patterns from data. Clustering as an important task of data mining divides the observations into groups (clusters), which is according to the principle that the observations in the same cluster are similar, and the ones from different clusters are dissimilar to each other. Subspace clustering enables clustering in subspaces within a ...

Reconstruction based subspace clustering methods compute a self reconstruction matrix over the samples and use it for spectral clustering to obtain the final clustering result. Their success largely relies on the assumption that the underlying subspaces are independent, which, however, does not always hold in the applications with increasing number of subspaces. In this paper, we propose a nove...

Nowadays, most streaming data sources are becoming highdimensional. Accordingly, subspace stream clustering, which aims at finding evolving clusters within subgroups of dimensions, has gained a significant importance. However, existing subspace clustering evaluation measures are mainly designed for static data, and cannot reflect the quality of the evolving nature of data streams. On the other ...

A model is developed for a non-uniform piezoelectric beam suitable for analyzing energy harvesting behavior. System dynamics are projected onto a numerically developed basis to produce energy functions which are used to derive equations of motion for the system. The resulting model reproduces the experimentally observed transition to chaos while providing a conservative estimate of power output...