报 告 人：Professor Xiaowei Xu University of Arkansas, Little Rock (UALR), USA
邀 请 人：赵 越
Big data is highly complex with many interrelated elements, which can be modeled as a graph or network by connecting the interrelated elements. Social networks include community groups based on common location, interests, occupation, etc. Finding an underlying network clustering in a network, if it exists, is important for a number of reasons. As the scale of real-world networks becomes increasingly larger, the existing network clustering algorithms fail to discover meaningful clusters efficiently. In this talk, we present a framework called AnySCAN, which applies anytime theory to the structural clustering algorithm for networks (SCAN). Moreover, an active learning strategy is proposed to advance the refining procedure in AnySCAN framework. AnySCAN with the active learning strategy is able to find the exact same clustering result on large-scale networks as the original SCAN in a significantly more efficient manner.
Xiaowei Xu, a professor of Information Science at the University of Arkansas, Little Rock (UALR), received his Ph.D. degree in Computer Science at the University of Munich in 1998. His research spans data mining, machine learning, bioinformatics, and high-performance computing. He has published over 100 papers in peer-reviewed journals and conference proceedings. With over 18,458 citations, he is one of the most cited researchers according to Google Scholar. His groundbreaking work on the density-based clustering algorithm DBSCAN has been widely used in textbooks and software implementations; and has received over 11,692 citations based on Google scholar to date. He is a recipient of the prestigious ACM SIGKDD Test of Time award for his contribution to the density-based clustering algorithm DBSCAN. Recently Dr. Xu has been recognized as a Most Influential Scholar in the field of Data Mining for his “outstanding and vibrant contributions to the field of Knowledge Discovery and Data Mining” by AMiner (https://aminer.org/mostinfluentialscholar/datamining).