In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

The aim of this paper is to present a novel subspace clustering method named FINDIT. Clustering is the process of finding interesting patterns residing in the dataset by grouping similar data objects from dissimilar ones based on their dimensional values. Subspace clustering is a new area of clustering which achieves the clustering goal in high dimension by allowing clusters to be formed with t...

Clustering techniques have been used on educational data to find groups of students who demonstrate similar learning patterns. Many educational data are relatively small in the sense that they contain less than a thousand student records. At the same time, each student may participate in dozens of activities, and this means that these datasets are high dimensional. Finding meaningful clusters f...

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

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 ...

In this paper we present Collaborative Low-Rank Subspace Clustering. Given multiple observations of a phenomenon we learn a unified representation matrix. This unified matrix incorporates the features from all the observations, thus increasing the discriminative power compared with learning the representation matrix on each observation separately. Experimental evaluation shows that our method o...

This paper presents the basic results for using the parallel coordinate representation as a high dimensional data analysis tool. Several alternatives are reviewed. The basic algorithm for parallel coordinates is laid out and a discussion of its properties as a projective transformation are shown. The several of the duality results are discussed along with their interpretations as data analysis ...