LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
Outlier Detection Using Inductive Logic Programming
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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Finding outliers is more interesting than finding common patterns in many KDD applications. The local outlier factor method (LOF) is a popular approach to detect outliers, in which a degree of being an outlier will be assigned to each object. In this paper, we present a modification method called WeightLOFCC to better handle outliers in time series data. Differing from the traditional LOF algorithm, the proposed WeightLOFCC method utilizes the idea of semi-supervised learning and weight factor to model data, and makes use of the cross correlation to measure the similarity. We evaluated the proposed algorithm on a large variety of data sets, and the experiment results show that for most of the data sets, our solution for outlier detection can achieve the best performance compared with other classical techniques.