Approximation algorithms for mining patterns from data streams

• Main Author: Dang, Xuan Hong
• Other Authors: Ng, Wee Keong
• Format: Theses and Dissertations
• Language: English
• Published: 2008
• Subjects: DRNTU::Engineering::Computer science and engineering::Information systems::Database management
• Online Access: https://hdl.handle.net/10356/13581
• Institution: Nanyang Technological University

Traditional data mining techniques expect all data to be managed within some form of persistent datasets. Recently, for many emerging applications, such as stock tickers, web-click streams, and telecom call records, the concept of a \textit{data stream} is more appropriate than a stored dataset. Naturally, a data stream is generated continuously in a dynamic environment with huge volume, infinite flow, and fast changing behaviors. Furthermore, they usually arrive to a mining system in a push-based manner meanwhile system resources used in the mining process are generally restricted in advance. Consequently, there have been increasing demands for developing novel techniques that are able to discover interesting patterns from data streams while they work within system resource constraints. Moreover, the mining results returned by these techniques are often desirable to be guaranteed within some error. When such an important task is completed, it is strongly believed that the quality of making decisions can be improved significantly in streaming environments. This research aims to study and investigate various approximation algorithms in order to effectively and efficiently mine useful patterns from data streams under different system resource constraints. Two fundamental data mining tasks are explored in the streaming data context: frequent pattern discovering and cluster analysis. The contributions of this research are claimed as follows: A novel algorithm named EStream is developed to address the problem of online mining frequent patterns from data streams with precise error guarantee.