We consider the problem of quantizing data generated from disparate sources, e.g. subjects performing actions with different styles, movies with particular genre bias, various con...
Ekaterina Taralova, Fernando DelaTorre, Martial He...
Accurate demand forecasting remains difficult and challenging in today's competitive and dynamic business environment, but even a little improvement in demand prediction may ...
Gaussian Mixture Model (GMM) is one of the most popular data clustering methods which can be viewed as a linear combination of different Gaussian components. In GMM, each cluster ...
We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property...
Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dim...
We present an application of bi-dimensional and heterogeneous time series clustering in order to resolve a Social Sciences issue. The dataset is the result of a survey involving mo...