Mong Li Lee

Mong Li Lee is an associate professor and assistant dean (Undergraduate Studies) in the School of Computing at the National University of Singapore. She received her PhD, MSc, and BSc (Hons 1) degrees in computer science from the National University of Singapore in 1999, 1992, and 1989, respectively. Her research interests include data cleaning, data mining, data integration of heterogeneous and semistructured data, and performance database issues in dynamic environments. She has published more than 100 technical research papers in international journals and conferences such as ACM SIGMOD, VLDB, ICDE, EDBT, and ACM SIGKDD. She has also served as a program committee member in various international conferences and is the editor of a number of books.

Publications

Temporal and Spatio-Temporal Data Mining
Temporal and Spatio-Temporal Data Mining
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 292 pages.
The recent surge of interest in spatio-temporal databases has resulted in numerous advances, such as: modeling, indexing, and querying of moving objects and spatio-temporal data....
Mining in Spatio-Temporal Databases
Mining in Spatio-Temporal Databases
Junmei Wang, Wynne Hsu, Mong Li Lee. © 2008. 16 pages.
Recent interest in spatio-temporal applications has been fueled by the need to discover and predict complex patterns that occur when we observe the behavior of objects in the...
Introduction
Introduction
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 13 pages.
Temporal databases capture time-related attributes whose values change with time, for example, stock exchange data. Temporal data mining is an important extension of data mining...
Time Series Mining: Background and Related Work
Time Series Mining: Background and Related Work
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 30 pages.
In this chapter, we will first give the background and review existing works in time series mining. The background material will include commonly used similarity measures and...
Mining Dense Periodic Patterns in Time Series Databases
Mining Dense Periodic Patterns in Time Series Databases
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 19 pages.
In this chapter, we describe a new periodicity detection algorithm to efficiently discover short period patterns that may exist in only a limited range of the time series. We...
Mining Sequence Patterns in Evolving Databases
Mining Sequence Patterns in Evolving Databases
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 24 pages.
In this chapter, we analyze and improve the I/O performance of the GSP algorithm (Agrawal & Srikant, 1996). We also study the problem of incremental maintenance of frequent...
Mining Progressive Confident Rules in Sequence Databases
Mining Progressive Confident Rules in Sequence Databases
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 25 pages.
Real-life objects can be described by its attribute values. For example, a person has attributes such as gender, date of birth, education level, and job, and so forth. While the...
Early Works in Spatio-Temporal Mining
Early Works in Spatio-Temporal Mining
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 18 pages.
Spatio-temporal data mining is an emerging area with increasing importance in a variety of applications, such as homeland security, mobile services, surveillance systems, and...
Mining Topological Patterns in Spatio-Temporal Databases
Mining Topological Patterns in Spatio-Temporal Databases
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 36 pages.
In this chapter, we study the problem of mining topological patterns by imposing temporal constraints into the process of mining collocation patterns. We first introduce a...
Mining Flow Patterns in Spatio-Temporal Data
Mining Flow Patterns in Spatio-Temporal Data
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 32 pages.
In this chapter, we describe flow patterns and the design of the algorithm called FlowMiner to find such flow patterns. FlowMiner incorporates a new candidate generation...
Mining Generalized Flow Patterns
Mining Generalized Flow Patterns
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 20 pages.
In this chapter, we investigate an efficient method to discover this class of relative-location sensitive flow patterns. These generalized flow patterns aim to summarize the...
Mining Spatio-Temporal Trees
Mining Spatio-Temporal Trees
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 18 pages.
We observe that many spatio-temporal trees patterns are both unordered and embedded. Unordered refers to the condition that the sequence between children of the same parent is...
Mining Spatio-Temporal Graph Patterns
Mining Spatio-Temporal Graph Patterns
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 35 pages.
Data mining in graph databases has received much attention. We have witnessed many algorithms proposed for mining frequent graphs. Inokuchi, Washio, and Nishimura (2002) and...
Conclusions and Future Work
Conclusions and Future Work
Wynne Hsu, Mong Li Lee, Junmei Wang. © 2008. 4 pages.
Association rule mining in spatial databases and temporal databases have been studied extensively in data mining research. Most of the research studies have found interesting...
Mining in Spatio-Temporal Databases
Mining in Spatio-Temporal Databases
Junmei Wang, Wynne Hsu, Mong Li Lee. © 2005. 22 pages.
Recent interest in spatio-temporal applications has been fueled by the need to discover and predict complex patterns that occur when we observe the behavior of objects in the...