59,036 research outputs found
Cluster detection and clustering with random start forward searches
The forward search is a method of robust data analysis in which outlier free subsets of the data of increasing size are used in model fitting; the data are then ordered by closeness to the model. Here the forward search, with many random starts, is used to cluster multivariate data. These random starts lead to the diagnostic identification of tentative clusters. Application of the forward search to the proposed individual clusters leads to the establishment of cluster membership through the identification of non-cluster members as outlying. The method requires no prior information on the number of clusters and does not seek to classify all observations. These properties are illustrated by the analysis of 200 six-dimensional observations on Swiss banknotes. The importance of linked plots and brushing in elucidating data structures is illustrated. We also provide an automatic method for determining cluster centres and compare the behaviour of our method with model-based clustering. In a simulated example with 8 clusters our method provides more stable and accurate solutions than model-based clustering. We consider the computational requirements of both procedures
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
Bayesian outlier detection in Capital Asset Pricing Model
We propose a novel Bayesian optimisation procedure for outlier detection in
the Capital Asset Pricing Model. We use a parametric product partition model to
robustly estimate the systematic risk of an asset. We assume that the returns
follow independent normal distributions and we impose a partition structure on
the parameters of interest. The partition structure imposed on the parameters
induces a corresponding clustering of the returns. We identify via an
optimisation procedure the partition that best separates standard observations
from the atypical ones. The methodology is illustrated with reference to a real
data set, for which we also provide a microeconomic interpretation of the
detected outliers
Steganographer Identification
Conventional steganalysis detects the presence of steganography within single
objects. In the real-world, we may face a complex scenario that one or some of
multiple users called actors are guilty of using steganography, which is
typically defined as the Steganographer Identification Problem (SIP). One might
use the conventional steganalysis algorithms to separate stego objects from
cover objects and then identify the guilty actors. However, the guilty actors
may be lost due to a number of false alarms. To deal with the SIP, most of the
state-of-the-arts use unsupervised learning based approaches. In their
solutions, each actor holds multiple digital objects, from which a set of
feature vectors can be extracted. The well-defined distances between these
feature sets are determined to measure the similarity between the corresponding
actors. By applying clustering or outlier detection, the most suspicious
actor(s) will be judged as the steganographer(s). Though the SIP needs further
study, the existing works have good ability to identify the steganographer(s)
when non-adaptive steganographic embedding was applied. In this chapter, we
will present foundational concepts and review advanced methodologies in SIP.
This chapter is self-contained and intended as a tutorial introducing the SIP
in the context of media steganography.Comment: A tutorial with 30 page
Splitting hybrid Make-To-Order and Make-To-Stock demand profiles
In this paper a demand time series is analysed to support Make-To-Stock (MTS)
and Make-To-Order (MTO) production decisions. Using a purely MTS production
strategy based on the given demand can lead to unnecessarily high inventory
levels thus it is necessary to identify likely MTO episodes.
This research proposes a novel outlier detection algorithm based on special
density measures. We divide the time series' histogram into three clusters. One
with frequent-low volume covers MTS items whilst a second accounts for high
volumes which is dedicated to MTO items. The third cluster resides between the
previous two with its elements being assigned to either the MTO or MTS class.
The algorithm can be applied to a variety of time series such as stationary and
non-stationary ones.
We use empirical data from manufacturing to study the extent of inventory
savings. The percentage of MTO items is reflected in the inventory savings
which were shown to be an average of 18.1%.Comment: demand analysis; time series; outlier detection; production strategy;
Make-To-Order(MTO); Make-To-Stock(MTS); 15 pages, 9 figure
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