Talks & Seminars
Title: DEMass: A Density Estimator for Large Data Sets
Prof. Kai-Ming Ting, Monash University
Date & Time: November 1, 2012 15:30
Venue: Conference Room, 01st Floor, C Block, Dept. of Computer Science & Engineering, Kanwal Rekhi Building
Density estimation is the ubiquitous base modelling mechanism employed for many tasks such as clustering, classification, anomaly detection and information retrieval. Commonly used density estimation methods such as kernel density estimator and k-nearest neighbour density estimator have high time and space complexities which render them inapplicable in problems with large data size and even a moderate number of dimensions. This weakness sets the fundamental limit in existing algorithms for all these tasks. We propose the first density estimation method which stretches this fundamental limit to an extent that dealing with millions of data can now be done easily and quickly. We analyse the error of the new estimation (from the true density) using a bias-variance analysis. We then perform an empirical evaluation of the proposed method by replacing existing density estimators with the new one in two current density-based algorithms, namely, DBSCAN, LOF and Bayesian classifier, representing three different data mining tasks of clustering, anomaly detection and classification, respectively. The results show that the new density estimation method significantly improves their time complexities, while maintaining or improving their task-specific performances in clustering, anomaly detection and classification. The new method empowers these algorithms, currently limited to small data size only, to process very large databases — setting a new benchmark for what density-based algorithms can achieve.
Speaker Profile:
After receiving his PhD from the University of Sydney, Australia, Kai Ming Ting had worked at the University of Waikato, New Zealand and Deakin University, Australia. He joins Monash University since 2001 and currently serves as the Associate Dean Research Training in Faculty of Information Technology and an Associate Professor in Gippsland School of Information Technology at Monash University. He had previously held visiting positions at Osaka University, Japan, Nanjing University, China, and Chinese University of Hong Kong. His current research interests are in the areas of mass estimation and mass-based approaches, ensemble approaches, data stream data mining, and swarm intelligence. He is an associate editor for Journal of Data Mining and Knowledge Discovery. He had co-chaired the Pacific-Asia Conference on Knowledge Discovery and Data Mining 2008. He had served as a member of program committees for a number of international conferences including ACM SIGKDD, IEEE ICDM and ICML. His research projects are supported by grants from Australian Research Council, US Air Force of Scientific Research (AFOSR/AOARD), Australian Institute of Sport, and Toyota InfoTechnology Center (Japan). Awards received include the Runner-up Best Paper Award in 2008 IEEE ICDM, and the Best Paper Award in 2006 PAKDD.
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