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KDD
2012
ACM
263views Data Mining» more  KDD 2012»
7 years 3 months ago
Integrating community matching and outlier detection for mining evolutionary community outliers
Temporal datasets, in which data evolves continuously, exist in a wide variety of applications, and identifying anomalous or outlying objects from temporal datasets is an importan...
Manish Gupta, Jing Gao, Yizhou Sun, Jiawei Han
CSDA
2011
8 years 4 months ago
Error rates for multivariate outlier detection
Multivariate outlier identification requires the choice of reliable cut-off points for the robust distances that measure the discrepancy from the fit provided by high-breakdown...
Andrea Cerioli, Alessio Farcomeni
IC3
2009
8 years 11 months ago
Local Subspace Based Outlier Detection
Abstract. Existing studies in outlier detection mostly focus on detecting outliers in full feature space. But most algorithms tend to break down in highdimensional feature spaces b...
Ankur Agrawal
ICDM
2010
IEEE
216views Data Mining» more  ICDM 2010»
8 years 11 months ago
Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams
The problem of finding outliers in data has broad applications in areas as diverse as data cleaning, fraud detection, network monitoring, invasive species monitoring, etc. While th...
Vit Niennattrakul, Eamonn J. Keogh, Chotirat Ann R...
IJSNET
2010
122views more  IJSNET 2010»
8 years 12 months ago
Ensuring high sensor data quality through use of online outlier detection techniques
: Data collected by Wireless Sensor Networks (WSNs) are inherently unreliable. Therefore, to ensure high data quality, secure monitoring, and reliable detection of interesting and ...
Yang Zhang, Nirvana Meratnia, Paul J. M. Havinga
KAIS
2007
120views more  KAIS 2007»
9 years 1 months ago
Capabilities of outlier detection schemes in large datasets, framework and methodologies
Abstract. Outlier detection is concerned with discovering exceptional behaviors of objects. Its theoretical principle and practical implementation lay a foundation for some importa...
Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, David W...
AIR
2004
131views more  AIR 2004»
9 years 1 months ago
A Survey of Outlier Detection Methodologies
Abstract. Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes ...
Victoria J. Hodge, Jim Austin
KAIS
2006
77views more  KAIS 2006»
9 years 1 months ago
Finding centric local outliers in categorical/numerical spaces
Outlier detection techniques are widely used in many applications such as credit card fraud detection, monitoring criminal activities in electronic commerce, etc. These application...
Jeffrey Xu Yu, Weining Qian, Hongjun Lu, Aoying Zh...
DATAMINE
2006
164views more  DATAMINE 2006»
9 years 1 months ago
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinica...
Matthew Eric Otey, Amol Ghoting, Srinivasan Partha...
CORR
2010
Springer
159views Education» more  CORR 2010»
9 years 1 months ago
Outlier Detection Using Nonconvex Penalized Regression
This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the n data points....
Yiyuan She, Art B. Owen
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