February 3, 1:30pm
Harnessing the power of self-organizing maps for high-dimensional large data analysis
High-dimensional data is increasingly becoming common because of its rich information content that can provide comprehensive characterization of objects (patterns) in real world situations. Unsupervised clustering aims to utilize this rich information content for detailed discovery of distinct patterns. However, conventional clustering methods may be inadequate for capturing intricate structure in high-dimensional and large data, due to their limitations. A powerful method in high-dimensional large data analysis is the Self-Organizing Map (SOM). An SOM is a neural learning algorithm that quantizes data spaces and spatially orders the quantization prototypes on a rigid lattice. The information learned by the SOM can be exploited to extract detailed cluster structure either by explanatory visualization or by clustering the SOM prototypes. Available SOM visualization or clustering schemes that are successful for relatively simple data often miss the finer structure in high-dimensional and large data. This seminar will present advanced SOM based visualization and clustering schemes for detailed/accurate cluster extraction.
Kadim Tasdemir received his B.S. degree from Bogazici University, Istanbul, Turkey, in 2001, his M.S. degree from Istanbul Technical University in 2003, and his Ph.D. degree from Rice University, Houston, TX, in 2008. He has been a researcher at the European Commission Joint Research Centre (JRC), Institute for Environment and Sustainability) since October 2009. Currently, he works on automated control methods for monitoring agricultural resources using remote sensing imagery. Before JRC, he worked as Assistant Professor at Department of Computer Engineering, Yasar University, Izmir, Turkey, in 2008-2009. During 2003-2008, he was a research assistant at Rice University, where he developed visualization and clustering methods using neural computation for detailed knowledge discovery, sponsored by NASA Applied Information Systems Research Program. He was also awarded “Rice University Robert Patten Award” for his contributions to graduate life. During 2001-2003, he was a research assistant at Istanbul Technical University, where he worked on License Plate Recognition project. His research interests include detailed knowledge discovery from high-dimensional and large data sets (especially remote sensing imagery) using machine learning and data mining, self-organized learning, computer vision and pattern recognition.