Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy
PhD Thesis
Title | Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy |
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Type | PhD Thesis |
Authors | |
Author | Zhang, Ji |
Supervisor | Gao, Qigang |
Wang, Hai | |
Institution of Origin | Dalhousie University, Canada |
Qualification Name | Doctor of Philosophy |
Number of Pages | 207 |
Year | 2008 |
Abstract | [Abstract]: Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large data sets. Most existing outlier detection methods only deal with static data with relatively low dimensionality. In this thesis, we present a new technique, called the Stream Project Outlier deTector (SPOT), which attempts to detect projected outliers in high-dimensional |
Keywords | Stream Projected Outlier deTector; SPOT; outlier detection |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Byline Affiliations | Dalhousie University, Canada |
https://research.usq.edu.au/item/9z29z/towards-outlier-detection-for-high-dimensional-data-streams-using-projected-outlier-analysis-strategy
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