Genetic Algorithms are heuristic search approaches that are applicable to a wide range of
optimization problems. This flexibility makes them attractive for many optimization problems …

Clustering is a difficult and widely studied data mining task, with many varieties of clustering
algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as …

Traditional subspace clustering aims to cluster data lying in a union of linear subspaces. The
vectorization of high-dimensional data to 1-D vectors to perform clustering ignores much of …

Subspace clustering technique divides the data set into different groups or clusters where
each cluster comprises of objects that share some similar properties. Again, the feature sets …

Subspace clustering techniques become paramount in pattern recognition for detecting local
variations from high dimensional data. Several techniques exist in the recent literature for …

High-dimensional data clustering is gaining attention in recent years due to its widespread
applications in many domains like social networking, biology, etc. As a result of the …

Recent technical advances have facilitated the massive acquisition of data described by a
large number of measurable properties (high dimensional datasets). New technologies have …

Subspace clustering techniques divide the data set into various groups, where each group is
represented by a subset of features known as subspace feature set, that are relevant to the …

L'origine évolutive de la complexité des systèmes biologiques interroge les sciences du
vivant depuis de nombreuses années. Dans cette thèse nous avons utilisé la plateforme …

We present a suite of tools under development for music creation and performance which
combines an evolving music generation algorithm with an evolving motion-sensitive …