7,747 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
In-Network Outlier Detection in Wireless Sensor Networks
To address the problem of unsupervised outlier detection in wireless sensor
networks, we develop an approach that (1) is flexible with respect to the
outlier definition, (2) computes the result in-network to reduce both bandwidth
and energy usage,(3) only uses single hop communication thus permitting very
simple node failure detection and message reliability assurance mechanisms
(e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data.
We examine performance using simulation with real sensor data streams. Our
results demonstrate that our approach is accurate and imposes a reasonable
communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on
Distributed Computing Systems 200
On Graph Stream Clustering with Side Information
Graph clustering becomes an important problem due to emerging applications
involving the web, social networks and bio-informatics. Recently, many such
applications generate data in the form of streams. Clustering massive, dynamic
graph streams is significantly challenging because of the complex structures of
graphs and computational difficulties of continuous data. Meanwhile, a large
volume of side information is associated with graphs, which can be of various
types. The examples include the properties of users in social network
activities, the meta attributes associated with web click graph streams and the
location information in mobile communication networks. Such attributes contain
extremely useful information and has the potential to improve the clustering
process, but are neglected by most recent graph stream mining techniques. In
this paper, we define a unified distance measure on both link structures and
side attributes for clustering. In addition, we propose a novel optimization
framework DMO, which can dynamically optimize the distance metric and make it
adapt to the newly received stream data. We further introduce a carefully
designed statistics SGS(C) which consume constant storage spaces with the
progression of streams. We demonstrate that the statistics maintained are
sufficient for the clustering process as well as the distance optimization and
can be scalable to massive graphs with side attributes. We will present
experiment results to show the advantages of the approach in graph stream
clustering with both links and side information over the baselines.Comment: Full version of SIAM SDM 2013 pape
Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma
A novel algorithm and implementation of real-time identification and tracking
of blob-filaments in fusion reactor data is presented. Similar spatio-temporal
features are important in many other applications, for example, ignition
kernels in combustion and tumor cells in a medical image. This work presents an
approach for extracting these features by dividing the overall task into three
steps: local identification of feature cells, grouping feature cells into
extended feature, and tracking movement of feature through overlapping in
space. Through our extensive work in parallelization, we demonstrate that this
approach can effectively make use of a large number of compute nodes to detect
and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion
simulation data, we observed linear speedup on 1024 processes and completed
blob detection in less than three milliseconds using Edison, a Cray XC30 system
at NERSC.Comment: 14 pages, 40 figure
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