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A comprehensive survey of anomaly detection techniques for high dimensional big data
Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …
that has various applications in the real world. However, many existing anomaly detection …
Change detection in streaming multivariate data using likelihood detectors
Change detection in streaming data relies on a fast estimation of the probability that the data
in two consecutive windows come from different distributions. Choosing the criterion is one …
in two consecutive windows come from different distributions. Choosing the criterion is one …
CMI: An information-theoretic contrast measure for enhancing subspace cluster and outlier detection
In many real world applications data is collected in multi-dimensional spaces, with the
knowledge hidden in subspaces (ie, subsets of the dimensions). It is an open research issue …
knowledge hidden in subspaces (ie, subsets of the dimensions). It is an open research issue …
Unsupervised anomaly detection for high dimensional data—An exploratory analysis
Context: Anomaly detection is a crucial area engaging the attention of many researchers. It
is a process of finding an unusual point or pattern in a given dataset. It is useful in many real …
is a process of finding an unusual point or pattern in a given dataset. It is useful in many real …
An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection
The accuracy of traditional anomaly detection techniques implemented on full-dimensional
spaces degrades significantly as dimensionality increases, thereby hampering many real …
spaces degrades significantly as dimensionality increases, thereby hampering many real …
Mix: A joint learning framework for detecting both clustered and scattered outliers in mixed-type data
Mixed-type data are pervasive in real life, but very limited outlier detection methods are
available for these data. Some existing methods handle mixed-type data by feature …
available for these data. Some existing methods handle mixed-type data by feature …
Development of a predictive model for Clostridium difficile infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory
Clostridium difficile infection is one of the major patient safety concerns in hospitals
worldwide. Clostridium difficile infection can have high economic burden to patients …
worldwide. Clostridium difficile infection can have high economic burden to patients …
Anomaly, novelty, one-class classification: a comprehensive introduction
AM Bartkowiak - … Journal of Computer Information Systems and …, 2011 - cspub-ijcisim.org
In data analysis and decision making we need frequently to judge whether the observed
data items are normal or abnormal. This happens in banking, credit card use, diagnosing …
data items are normal or abnormal. This happens in banking, credit card use, diagnosing …
Energy-based anomaly detection for mixed data
Anomalies are those deviating significantly from the norm. Thus, anomaly detection amounts
to finding data points located far away from their neighbors, ie, those lying in low-density …
to finding data points located far away from their neighbors, ie, those lying in low-density …
Finding key attribute subset in dataset for outlier detection
P Yang, Q Zhu - Knowledge-based systems, 2011 - Elsevier
Detection of outlier from high dimensional dataset have found important applications in
many fields, yet the unexpected time consumption is likely to hinder its practical use. Thus, it …
many fields, yet the unexpected time consumption is likely to hinder its practical use. Thus, it …