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Modeling and Efficient Mining of Intentional Knowledge of Outliers
Hong Kong, SAR July 16-July 18
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IDEAS.2003.1214910Seventh International Database Engine ...
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Zhixiang Chen, University of Texas-Pan American
Jian Tang, Chinese University of Hong Kong
Ada Wai-Chee Fu, Chinese University of Hong Kong
In this paper, we study in a general setting the notion of outliered patterns as intentional knowledge of outliers and algorithms to mine those patterns. Our contributions consist of a model for de.ning outliered patterns with the help of categorical and behavioral similarities of outliers, and efficient algorithms for mining knowledge sets of distance-based outliers and outliered patterns. Our algorithms require only very limited domain knowledge, and no classified information. We also present an empirical study to show the feasibility of our algorithms.
Index Terms:
outlier detection, knowledge sets, categorical similarity, behavioral similarity, outliered patterns
Citation:
Zhixiang Chen, Jian Tang, Ada Wai-Chee Fu, "Modeling and Efficient Mining of Intentional Knowledge of Outliers," ideas, pp.44, Seventh International Database Engineering and Applications Symposium (IDEAS'03), 2003
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