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    » Outlier Detection for High Dimensional Data
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    IJON
    2006
    85views more  IJON 2006»
    From outliers to prototypes: Ordering data
    13 years 6 months ago
    From outliers to prototypes: Ordering data
    Download  www.kyb.tuebingen.mpg.de
    We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show ...
    Stefan Harmeling, Guido Dornhege, David M. J. Tax,...
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    ICDE
    2008
    IEEE
    161views Database» more  ICDE 2008»
    Trajectory Outlier Detection: A Partition-and-Detect Framework
    14 years 7 months ago
    Trajectory Outlier Detection: A Partition-and-Detect Framework
    Download  www.cs.uiuc.edu
    Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlie...
    Jae-Gil Lee, Jiawei Han, Xiaolei Li
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    KDD
    2012
    ACM
    235views Data Mining» more  KDD 2012»
    A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data
    11 years 8 months ago
    A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data
    Download  www.itu.dk
    Outlier mining in d-dimensional point sets is a fundamental and well studied data mining task due to its variety of applications. Most such applications arise in high-dimensional ...
    Ninh Pham, Rasmus Pagh
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    GFKL
    2004
    Springer
    137views Data Mining» more  GFKL 2004»
    Density Estimation and Visualization for Data Containing Clusters of Unknown Structure
    13 years 11 months ago
    Density Estimation and Visualization for Data Containing Clusters of Unknown Structure
    Download  www.mathematik.uni-marburg.de
    Abstract. A method for measuring the density of data sets that contain an unknown number of clusters of unknown sizes is proposed. This method, called Pareto Density Estimation (PD...
    Alfred Ultsch
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    SDM
    2003
    SIAM
    184views Data Mining» more  SDM 2003»
    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
    13 years 7 months ago
    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
    Download  www-users.cs.umn.edu
    The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
    Levent Ertöz, Michael Steinbach, Vipin Kumar
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