Streaming data have gained considerable attention in database and
data mining communities because of the emergence of a class of
applications, such as financial marketing, sensor networks, internet
IP monitoring, and telecommunications that produce these data. Data
streams have some unique characteristics that are not exhibited by
traditional data: unbounded, fast-arriving, and time-changing.
Traditional data mining techniques that make multiple passes over
data or that ignore distribution changes are not applicable to
dynamic data streams. Mining data streams has been an active
research area to address requirements of the streaming applications.
This thesis focuses on developing techniques for distribution change
detection and mining time-changing data streams. Two techniques are
proposed that can detect distribution changes in generic data
streams. One approach for tackling one of the most popular stream
mining tasks, frequent itemsets mining, is also presented in this
thesis. All the proposed techniques are implemented and empirically
studied. Experimental results show that the proposed techniques can
achieve promising performance for detecting changes and mining
dynamic data streams.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/6374 |
| Date | January 2011 |
| Creators | Tao, Yingying |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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