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Abstract: Identification of outliers can lead to the discovery of unexpected, interesting, and implicit knowledge. Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We define tests for spatial outliers in graph structured data sets, analyze the statistical foundation underlying our approach, design a fast algorithm to detect spatial ... (Update)
Context of citations to this paper: More
.... Mobile computing devices, e.g. personal digital assistants (PDA) and in car navigation units, require access to spatial datasets [5, 10, 11, 13, 14] such as vector maps for locationbased services. An example of a query would be Where is the nearest gas station Vector maps,...
...information from the vast amount of data they have gathered, thus helping them make more effective decisions. Spatial data mining [18, 19, 26, 25, 5], a subfield of data mining, is concerned with the discovery of interesting and This work is partially supported by the Army...
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BibTeX entry: (Update)
S. Shekhar, C.T. Lu, and P Zhang. Detecting Graph-based Spatial Outliers: Algorithms and Applications . In Department of Computer Science Techinical Report TR 01-014, University of Minnesota: http://tiberius.cs.umn.edu/techreports /listing/, 2001. http://citeseer.ist.psu.edu/article/shekhar01detecting.html More
@inproceedings{ shekhar01detecting,
author = "Shashi Shekhar and Chang-Tien Lu and Pusheng Zhang",
title = "Detecting graph-based spatial outliers: algorithms and applications (a summary of results)",
booktitle = "the Seventh SIGKDD Int'l Conference on Knowledge Discovery and Data Mining (KDD 2001)",
pages = "371-376",
year = "2001",
url = "citeseer.ist.psu.edu/article/shekhar01detecting.html" }
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