Return to search

Frequent pattern mining of uncertain data streams

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.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:MWU.1993/5233
Date January 2011
CreatorsJiang, Fan
ContributorsLeung, Carson K. (Computer Science), Domaratzki, Michael (Computer Science) Wang, Xikui (Statistics)
PublisherSpringer-Verlag, ACM
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
Detected LanguageEnglish

Page generated in 0.0021 seconds