| Lecturer Profile |
Dr. Ling Chen is a senior lecturer with the Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney. She received Ph.D. from Nanyang Technological University, Singapore. Before joining UTS, she was a postdoc research fellow with L3S Research Center, University of Hannover, Germany. Her research interests include data mining and machine learning, social network analysis and recommender systems. She has published more than 50 papers in major conferences and journals including SIGKDD, ICDM, SDM, WWW and ACM TOIS. |
| Lecture Abstract |
Abstract: Transaction data mining is ubiquitous in various domains and has been researched extensively. In recent years, observing that uncertainty is inherent in many real world applications, uncertain data mining has attracted much research attention. Among the research problems, summarization is important because it produces concise and informative results, which facilitates further analysis. However, there are few works exploring how to effectively summarize uncertain transaction data. In this work, we formulate the problem of summarizing uncertain transaction data as Minimal Probabilistic Tile Cover Mining, which aims to find a high-quality probabilistic tile set covering an uncertain database with minimal cost. We define the concept of Probabilistic Price and Probabilistic Price Order to evaluate and compare the quality of tiles, and propose a framework to discover the minimal probabilistic tile cover. The bottleneck is to check whether a tile is better than another according to the Probabilistic Price Order, which involves the computation of a joint probability. We prove that it can be decomposed into independent terms and calculated efficiently. Several optimization techniques are devised to further improve the performance. Experimental results on real world datasets demonstrate the conciseness of the produced tiles and the effectiveness and efficiency of our approach. |
