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|Title: ||Frequent pattern mining of uncertain data streams|
|Authors: ||Jiang, Fan|
|Supervisor: ||Leung, Carson K. (Computer Science)|
|Examining Committee: ||Domaratzki, Michael (Computer Science)
Wang, Xikui (Statistics)|
|Graduation Date: ||May 2012|
|Keywords: ||Data mining|
|Issue Date: ||2011|
|Citation: ||Leung, C.K.-S., Jiang, F. (2011) Frequent pattern mining from time-fading streams of uncertain data. In Proc. DaWaK 2011: 252-264. Springer.|
Leung, C.K.-S., Jiang, F., Hayduk, Y. (2011) A landmark-model based system for mining frequent patterns from uncertain data streams. In Proc. IDEAS 2011: 249-250. ACM.
Leung, C.K.-S., Jiang, F. (2011) Frequent itemset mining of uncertain data streams using the damped window model. In Proc. ACM SAC 2011: 950-955. ACM.
|Abstract: ||When dealing with uncertain data, users may not be certain about the presence of an item in the database. For example, due to inherent instrumental imprecision or errors, data collected by sensors are usually uncertain. In various real-life applications, uncertain databases are not necessarily static, new data may come continuously and at a rapid rate. These uncertain data can come in batches, which forms a data stream. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, algorithms have been developed to use the sliding window model for processing and mining data streams. However, for some applications, the landmark window model and the time-fading model are more appropriate. In this M.Sc. thesis, I propose tree-based algorithms that use the landmark window model or the time-fading model to mine frequent patterns from streams of uncertain data. Experimental results show the effectiveness of our algorithms.|
|Appears in Collection(s):||FGS - Electronic Theses & Dissertations (Public)|
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