Outlier Detection
An Evaluation of Classification and Outlier Detection Algorithms
... There are many data sets available for classification evalua- tions, see (Bagnall et al. 2017) for a list. However, there are very few time-series datasets amenable to both outlier detec- tion and classification ...
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Outlier Detection Methods for Industrial Applications
... bulk outlier detection method for multivariate data and Donoho- Stahel uses the outlyingness measure compute by the Donoho-Stahel (Rousseeuw & Leroy, 1997) estimator of location and ...scattered ...
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Outlier Detection Approaches in Data Mining
... most existing study concentrate on the algorithm based on special background, compared with outlier identification approach is comparatively less. This paper mainly discusses about outlier detection ...
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Outlier Detection in Accounting
... of outlier detection were applied, with the purpose of optimizing the ratio between the cost of manual validation and the impact in the quality of the ...Local Outlier Factor (LOF) and ...
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Scalable Techniques for Trajectory Outlier Detection
... Real-time detection of trajectory outliers play an important role in making key decisions in various applications because these anomalies can have a critical and significant ...Similarly, outlier ...
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ABSTRACT : Outlier detection is the process of finding outlying pattern from a given dataset. Outlier detection
... a outlier ranking based on the objects deviation in a set of relevant subspace ...of outlier scores provided by various outlier models and a translation of the arbitrary “outlier factors” to ...
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Unknown word sense detection as outlier detection
... of outlier detection is to de- cide whether a new object belongs to a given training set or ...Typically, outlier detection approaches derive some boundary around the training set, or they ...
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Outlier Detection with Nonlinear Projection Pursuit
... for outlier detection are parametric methods: given a certain kind of statistical distribution outliers are detected as those points with low probability of being gen- ...
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Outlier Detection in GARCH Models
... Financial data typically show volatility clustering and so-called thick tails. The ARCH (Engle, 1982) and GARCH (Bollerslev, 1986) models were designed to capture these features. However, when estimating a GARCH model ...
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Outlier Detection in BLAST Hits
... an outlier detection method for taxonomy assignment using BLAST hits that separates phylogenetically correct matches from matches to sequences from similar but phylogenetically more distant ...
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On the internal evaluation of unsupervised outlier detection
... of outlier detection results, that is, the assessment of the quality of results of unsupervised outlier de- tection methods without refering to external information (such as class ...of ...
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New methods in outlier detection
... of outlier groups (Definition ...an outlier degree ...among outlier groups, reference groups and contexts more ...contextual outlier detection is another challenge for future ...
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A Comparative Study on Outlier Detection Techniques
... in outlier detection is that it involves exploring the unseen ...an outlier can be defined as a pattern that does not conform to expected normal ...
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Outlier detection on network flow analysis
... unsupervised outlier detection algorithms in detecting DDoS ...the outlier detection algorithms perform, particularly Isolation Forest or PCA-based algorithms, perform best if the proportion ...
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EVALUATION OF OUTLIER DETECTION FOR TRAJECTORY DATA
... of outlier and after removing of outlier for accuracy and Silhouette are presented in figure 5, 6, 7, and 8 ...after outlier detection, accuracy and silhouette value are ...of outlier ...
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OUTLIER DETECTION USING WEIGHTED HOLOENTROPY
... for outlier detection but all calculate the nearest neighbours of a record using a suitable distance calculation metric such as Euclidean distance or Mahalanobis ...
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A Review on Various Outlier Detection Techniques
... outliers detection are two important aspects in distance based outlier detection ...local outlier over streaming ...Local Outlier Factor-LOF value is ...
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Outlier detection for patient monitoring and alerting
... their detection would lead to useful clin- ical ...by outlier detection methods will identify errors often en- ough to be worth alerting ...statistical outlier detection is positively ...
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Comparative Analysis of Outlier Detection Techniques
... Manhattan distance and Euclidean distance metrics for measuring the distance, some non-metric distance functions are also used for making the distance based definitions of outliers very general. For sequences, a distance ...
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Outlier Detection for Multidimensional Medical Data
... An outlier [5], [6] is defined as a data point which is very different from the rest of the data based on some ...the outlier detection module to determine the ...
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