Large Scale Clustering Algorithms 2016 (LSCA’16)

This special session aims to promote new advances and research directions to address the clustering problem in large scale practical applications.

Unprecedented technological advances lead to increasingly large scale data sets in all areas of science, engineering and businesses. These include genomics and proteomics, biomedical imaging, signal processing, astrophysics, finance, web and market basket analysis, among many others. The number of such data is often of the order of millions or billions. Classical clustering algorithms become inadequate, questionable, or inefficient at best, and this calls for new clustering algorithms.

Topics of interest include theoretical foundations, algorithms and implementation, as well as applications and empirical studies, for example:

·         Systematic studies of how the number of samples affects clustering algorithms;

·         New clustering algorithms;

·         Research on parallel clustering algorithms;

·         Multiple clustering algorithm ensemble;

·         Methods of random projections, compressed sensing, and random matrix theory applied to large scale clustering;

·         Theoretical underpinning of large scale clustering algorithms;

·         Data presentation and visualisation methods for large scale datasets;

·         Applications to real problems in science, engineering or businesses where the data is large.

LSCA’16 will be co-located with IEEE World Congress on Computational Intelligence (IEEE WCCI) which will be held in the magnificent city of Vancouver, Canada on 25-29, July 2016.



Please go to the IJCNN/WCCI submission website, and select "Sx. Research on Large Scale Clustering Algrithms" topic in the "Main research topic*" field.


Yiming Zhang

National University of Defense Technology, China

Yiming Zhang received the BSc degree and M.Sc. degree in Mechanics Engineering in 2001 and 2003, and the Ph.D. degree in Computer Science in 2008, all from the Chinese National University of Defense Technology (NUDT), Changsha, Hunan, China. Dr. Zhang received the China Computer Federation (CCF) Distinguished Dissertation Award in 2010. He has published more than 20 books and more than 30 technical papers in journals and conference proceedings. He is the Program Chair for iVCE Workshop 2016 and was ICDCS’11 PC member and P2P’10 external PC member. He has participated in more than 10 industrial projects and helped to develop many commercial systems and software tools. He was a visiting professor at Microsoft Research Asia in 2011 and at the computer lab of University of Cambridge in 2012 and 2013. He is currently an associate professor at School of Computer, NUDT. His current research interests include cloud-based machine learning.


Shaohe Lv

National University of Defense Technology, China

Shaohe Lv received the B.Sc., M.Sc. and Ph.D. degrees from National University of Defense Technology, China, all in computer science. He was a visiting scholar at University of Waterloo from Dec. 2008 to Dec. 2009 and at National University of Singapore from Aug. 2015 to Nov. 2015. In July 2011, he joined the School of Computer, National University of Defense Technology, as an Assistant Professor. He, as the first author, received a best paper award from IEEE ICC 2012 (International Conference on Communications). His current research focuses on big data analysis, machine learning and ubiquitous computing. He was a Technical Program Committee (TPC) member for IEEE ICC and GLOBECOM (2012-2016). He is a member of IEEE, ACM and CCF.


Xin Niu

National University of Defense Technology, China

Xin Niu received the B.S. degree in computer science and technology in 2006 from the National University of Defense Technology (NUDT), Changsha, China, and the Doctor degree in geoinformatics in 2012 from Royal Institute of Technology-KTH, Stockholm, Sweden. Since 2013, he has joined National Laboratory for Parallel and Distributed Processing (PDL) in NUDT as an assistant professor. His research interests include remote sensing image processing, machine learning and parallel computing.


Xinwang Liu

National University of Defense Technology, China

Xinwang Liu received the M. Eng. and Ph. D. degree from National University of Defense Technology, China in 2008 and 2013, respectively. From Jan. 2014, He works as a research assistant at National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China. His research interests focus on designing algorithms on kernel learning, feature selection and multi-view clustering.