| Abstract: | All fields of scientific research have experienced an explosion of data. It is a formidable computational challenge to analyze this data to extract unexpected patterns. Meeting this challenge will require new, advanced methods of analysis. Dynamic Quantum Clustering is such a new and very different approach to attacking this problem. The algorithm, invented by David Horn (Tel Aviv University) and myself, provides a highly visual and interactive tool that allows one to explore complicated data that has no known structure. It is like panning for gold, when you don't know gold exists. My talk will provide a brief introduction to the distinction between supervised and unsupervised methods in data mining (clustering in particular). Then, I will, very briefly, discuss the theory of DQC and and some applications in bio-informatics. The bulk of my talk will be devoted to showing new results on a data set coming from the Stanford Synchrotron Radiation Laboratory. This example shows the power of DQC applied to data sets on which the currently most favored unsupervised data mining techniques fail to obtain any interesting results. |
