The rapid accumulation of large collections of data has created the need for efficient and intelligent schemes for knowledge extraction and results analysis. The resulting informa...
We present a computational framework to automatically discover high-order temporal social patterns from very noisy and sparse location data. We introduce the concept of social foo...
Abstract. As the amount of information available to users continues to grow, filtering wanted items from unwanted ones becomes a dominant task. To this end, various collaborative-f...
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...
Analysis of biological data often involves large data sets and computationally expensive algorithms. Databases of biological data continue to grow, leading to an increasing demand ...