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This thesis focuses on designing spectral tools for graph clustering in sublinear time. With the emergence of big data, many traditional polynomial time, and even linear time algorithms have become prohibitively expensive. Processing modern datasets requir ...
We study the problem of constructing epsilon-coresets for the (k, z)-clustering problem in a doubling metric M(X, d). An epsilon-coreset is a weighted subset S subset of X with weight function w : S -> R->= 0, such that for any k-subset C is an element of ...
In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on th ...
The problem of clustering in urban traffic networks has been mainly studied in static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be ...
We consider the problem of decentralized clustering and estimation over multitask networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which a ...
The problem of clustering in urban traffic networks has been mainly studied in static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be ...
In Paralympic cross-country sit skiing, athlete classification is performed by an expert panel, so it may be affected by subjectivity. An evidence-based classification is required, in which objective measures of impairment must be identified. The purposes ...
This master thesis provides an extended study of winter precipitations in Valais (Switzerland). It was rst tried to relate local conditions in Valais, as measured at gauges, to the Hess-Brezowski classication for Europe. It was not possible to clearly rela ...
In this paper, we introduce an evolving system utilizing sparse weighted kernel least square as local models and online Gustafson-Kessel clustering algorithm for structure identification. Our proposed online clustering algorithm forms elliptical clusters w ...
Clustering algorithms have evolved to handle more and more complex structures. However, measures allowing to qualify the quality of such partitions are rare and only specic to certain algorithms. In this work, we propose a new cluster validity measure (CVM ...