10,857 research outputs found
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
Anomaly Detection on Graph Time Series
In this paper, we use variational recurrent neural network to investigate the
anomaly detection problem on graph time series. The temporal correlation is
modeled by the combination of recurrent neural network (RNN) and variational
inference (VI), while the spatial information is captured by the graph
convolutional network. In order to incorporate external factors, we use feature
extractor to augment the transition of latent variables, which can learn the
influence of external factors. With the target function as accumulative ELBO,
it is easy to extend this model to on-line method. The experimental study on
traffic flow data shows the detection capability of the proposed method
Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees
This paper investigates an important problem in stream mining, i.e.,
classification under streaming emerging new classes or SENC. The common
approach is to treat it as a classification problem and solve it using either a
supervised learner or a semi-supervised learner. We propose an alternative
approach by using unsupervised learning as the basis to solve this problem. The
SENC problem can be decomposed into three sub problems: detecting emerging new
classes, classifying for known classes, and updating models to enable
classification of instances of the new class and detection of more emerging new
classes. The proposed method employs completely random trees which have been
shown to work well in unsupervised learning and supervised learning
independently in the literature. This is the first time, as far as we know,
that completely random trees are used as a single common core to solve all
three sub problems: unsupervised learning, supervised learning and model update
in data streams. We show that the proposed unsupervised-learning-focused method
often achieves significantly better outcomes than existing
classification-focused methods
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
Outlier Detection Techniques For Wireless Sensor Networks: A Survey
In the field of wireless sensor networks, measurements that
significantly deviate from the normal pattern of sensed data are
considered as outliers. The potential sources of outliers include
noise and errors, events, and malicious attacks on the network.
Traditional outlier detection techniques are not directly
applicable to wireless sensor networks due to the multivariate
nature of sensor data and specific requirements and limitations of
the wireless sensor networks. This survey provides a comprehensive
overview of existing outlier detection techniques specifically
developed for the wireless sensor networks. Additionally, it
presents a technique-based taxonomy and a decision tree to be used
as a guideline to select a technique suitable for the application
at hand based on characteristics such as data type, outlier type,
outlier degree
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