Name
Abstract | ||
|---|---|---|
Compared to relational data models, the hierarchical structure of semi-structured data such as XML provides semantically meaningful neighbourhoods advancing data cleaning problems such as outlier detection. In this paper, we introduce the concept of correlated subspace that leverages on the hierarchical relationships between XML attributes to provide contextually informative neighbourhoods for attribute outlier detection. We also design two correlation-based attribute outlier metrics for XML, namely the xO-Measure and xQ-Measure. The effectiveness of our XML outlier detection approach is supported with experimental results. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICDE.2008.4497610 | Cancun |
Keywords | Field | DocType |
outlier detection,xml outlier detection approach,hierarchical relationship,attribute outlier detection,semi-structured data,hierarchical structure,correlation-based attribute outlier metrics,correlation-based attribute outlier detection,contextually informative neighbourhood,data model,correlated subspace,data models,clustering algorithms,pattern analysis,xml,data cleaning,semi structured data,noise,navigation,relational data model,data structures | Data mining,Anomaly detection,Data structure,Data modeling,Object detection,XML,Relational database,Computer science,Outlier,Cluster analysis,Database | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-4244-1837-4 | 6 |
PageRank | References | Authors |
0.47 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J L Koh | 1 | 110 | 14.39 |
| Mong Li Lee | 2 | 2031 | 267.53 |
| Wynne Hsu | 3 | 3878 | 353.89 |
| Wee Tiong Ang | 4 | 82 | 6.43 |