NCeSS Project : Data mining for social scientists

We will discuss the work being undertaken on the NCeSS data mining project, a one year project at the University of Manchester which began at the start of 2007, to develop data mining tools of value to the social science community. Our primary goal is to produce a suite of data mining codes, supported by a web interface, to allow social scientists to mine their datasets in a straightforward way and hence, gain new insights into their data. In order to fully define the requirements, we are looking at a range of typical datasets to find out what forms they take and the applications and algorithms that will be required. In this paper, we will describe a number of these datasets and will discuss how easily data mining techniques can be used to extract information from the data that would either not be possible or would be too time consuming by more standard methods

SHREC'16: partial matching of deformable shapes

Matching deformable 3D shapes under partiality transformations is a challenging problem that has received limited focus in the computer vision and graphics communities. With this benchmark, we explore and thoroughly investigate the robustness of existing matching methods in this challenging task. Participants are asked to provide a point-to-point correspondence (either sparse or dense) between deformable shapes undergoing different kinds of partiality transformations, resulting in a total of 400 matching problems to be solved for each method - making this benchmark the biggest and most challenging of its kind. Five matching algorithms were evaluated in the contest; this paper presents the details of the dataset, the adopted evaluation measures, and shows thorough comparisons among all competing methods