Abstract
In this paper, we present an alternative approach to discover interesting unusual observations that can not be discovered by outlier detection techniques. The unusual pattern is determined according to the deviation of a group of observations from other observations and the number of observations in the group. To measure the degree of deviation, we introduce the concept of adaptive nearest neighbors that captures the natural similarity between two observations. The boundary points determined by the adaptive nearest neighbor algorithm are used to adjust the level of granularity. The adaptive nearest neighbors are then used to cluster the data set. Finally, we ran experiments on a real life data set to evaluate the result. According to the experiments, we discovered interesting unusual patterns that are overlooked by using outlier detection and clustering algorithms.
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Nguyen, M.Q., Mark, L., Omiecinski, E. (2008). Unusual Pattern Detection in High Dimensions. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_23
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DOI: https://doi.org/10.1007/978-3-540-68125-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
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