Day 2 :
King Abdullah University of Science and Technology, Saudi Arabia
Time : 9:30-10:15
Mikhail Moshkov is a Professor in the CEMSE Division at King Abdullah University of Science and Technology, Saudi Arabia. He earned his Master’s degree from Nizhni Novgorod State University, received his Doctorate from Saratov State University, and Habilitation from Moscow State University. In 2003, he has worked at the Institute of Computer Science, University of Silesia, in Poland. His main areas of research are Complexity of Algorithms, Combinatorial Optimization, and Machine Learning. He is has published 5 research papers in Springer.
In the presentation, we consider extensions of dynamic programming approach to the study of decision trees as algorithms for problem solving; as a way for knowledge extraction and representation, and as classifiers which for a new object given by values of conditional attributes, define a value of the decision attribute. These extensions allow us : (i) To describe the set of optimal decision trees; (ii) To count the number of these trees; (iii) To make sequential optimization of decision trees relative to different criteria; (iv) To find the set of Pareto optimal points for two criteria; and (v) To describe relationships between two criteria. The results include the minimization of average depth for decision trees sorting eight elements (this question was open since 1968), improvement of upper bounds on the depth of decision trees for diagnosis of 0-1 faults in read-once combinatorial circuits; existence of totally optimal (with minimum depth and minimum number of nodes) decision trees for Boolean functions; study of time-memory tradeoff for decision trees for corner point detection; study of relationships between number and maximum length of decision rules derived from decision trees; study of accuracy-size tradeoff for decision trees which allows us to construct enough small and accurate decision trees for knowledge representation; and decision trees that as classifiers, outperform often decision trees constructed by CART. The end of the presentation is devoted to the introduction to KAUST.
Swansea University, UK
Keynote: Data mining with data visualization
Time : 10:15-11:00
Robert S Laramee received a Bachelor’s degree in Physics from the University of Massachusetts, Amherst. In 2000, he received a Master’s degree in Computer Science from the University of New Hampshire, Durham. He was awarded a PhD from the Vienna University of Technology, Austria at the Institute of Computer Graphics and Algorithms in 2005. From 2001 to 2006 he was a Researcher at the VRVis Research Center (www.vrvis.at) and a Software Engineer at AVL (www.avl.com) in the Department of Advanced Simulation Technologies. Currently, he is an Associate Professor in the Department of Computer Science at the Swansea University, Wales. His research interests are in the areas of Big Data Visualization, Visual Analytics, and Human-Computer Interaction. He has published more than 100 peer-reviewed papers in scientific.
Some people believe that we live in the age of information. I believe it’s much more accurate to say we live in the age of data. With the rapid advancement of big data storage technologies and the ever-decreasing costs of hardware, our ability to derive and store data is unprecedented. However, a large gap remains between our ability to generate and store large collections of complex, time-dependent data and our ability to derive useful information and knowledge from it. Data visualization leverages our most powerful sense, vision, in order to derive knowledge and gain insight into large, multivariate data sets that describe complicated and often time-dependent behavior. This talk presents data mining from the perspective of data visualization with three very different applications: Computational Fluid Dynamics (CFD), marine biology and rugby, showcasing some of visualizations strengths, weaknesses and goals. Data visualization is critical to successful data mining and extracting knowledge and insight from big data.