IEEE Systems, Man and Cybernetics Magazine - April 2020 - 42

by human experts. They used iVAT to visualize the time
series (each time series is represented by 16 different RQA
measures, which are used to calculate a Euclidean distance matrix) and found that the iVAT visualization of cluster structure was interpretable and consistent with the
clustering structure based on the expert opinion.
Iredale et  al. [155] proposed a novel shape-based measure of similarity, which is invariant under uniform time
shift and uniform amplitude scaling. Using this measure to
calculate the distance matrix, the authors used the VAT
algorithm to assess large time-series data sets and demonstrated its advantages in terms of complexity and propensity for implementation in a distributed computing
environment. In the field of neuroscience research, Mahallati et al. [156] experimented on a variety of distance measures-Euclidean distance, correlation distance, DTW, and
shape-based distance-in the recording of extracellular
action potential (spike) waveforms generated by neuronal
activity, using iVAT to distinguish the number of units present within recordings from a single electrode.
WSNs for Environmental Monitoring
Understanding the behavior of complex ecosystems
requires analysis of detailed observations of an environment under a range of different conditions. WSNs provide a
flexible platform to collect data for environmental modeling. A WSN comprises a set of low-powered nodes, each
with its sensors, power supply, CPU, and radio transceiver,
which can self-organize into a network for collecting and
reporting sensor measurements. Although WSNs provide
raw data from the monitored environment, an open challenge is how to build and utilize models of "normal" behavior and "interesting" (anomalous) events from that data.
A series of papers by Bezdek et al. [157], [158], Moshtaghi
et al. [159], and Rajasegarar et al. [160] used hyperellipsoidal
models and VAT/iVAT visualization to detect anomalies in a
WSN for environmental monitoring. The proposed approach
generates a set of hyperellipsoids to summarize the data generated by the WSN. These papers proposed three measures
of similarity between pairs of ellipsoids (compound, transformation energy, and Bhattacharya coefficient similarity) to
convert model ellipsoids into dissimilarity data, which was
then fed to VAT/iVAT to discover clusters in the data. Finally,
the authors used various clustering algorithms (SL, CLODD,
and so on) to extract normal clusters and anomalies from
the input data. This framework was empirically evaluated on
a variety of data sets, viz., data collected by a WSN installed
at the Intel Berkeley Research Lab to measure parameters
(e.g., humidity, temperature, light, and so on) and the Heron
Island WSN in the Great Barrier Reef, among others.
Miscellaneous Applications
Humans and Society
In the psychology of motivation, balance theory is a theory
of attitude change proposed by Fritz Heider [161]. Heider's
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IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Apri l 2020

structural balance theory explains social processes and is
used to account for social actors' attitudes toward one
another. Notsu et al. [162] propose a new social value emergence model in the form of an agent-based simulation model.
In this model, structural balance theory is used to explain
feelings, attitudes, and beliefs. Each agent tries to reach balanced states and communicates with others. The VAT algorithm was used to understand the agent group's macrolevel
mechanism and represents the social groups to which different agents belong. As an extension to their previous work,
Notsu et al. [163] adapted VAT to a network model by reinterpreting positive/negative relationships in naive psychology as dissimilarity, such as "near" or "similar"/"distant" or
"unlike" to improve mutual understanding among people.
Human Geography
Human geography is concerned with how human-related factors, such as cultural, economic, religious, and political
issues, influence the spatial behavior of individuals and
groups of people. The study of human geography is important
for several application areas, including, for example, preparing for disaster response and relief, identifying medically
underserved areas, and so on. Buck et al. [164] proposed a
VAT-based approach to summarizing various human-related
factors that can be mapped for a geographic region for visualization and easy understanding by domain experts. To combine various human geographic factors for a region, a human
geography data cube (consisting of spatial dimensions of the
region and one human-geographic dimension) was created.
Different data cubes (belonging to different spatial locations)
were then visualized using VAT to present a complete picture
of the spatial distribution of various human-related factors
and their interactions with each other.
Instance-Based Machine Learning
In instance-based machine learning (e.g., nearest-neighbor
classifiers), algorithms often suffer from high storage
requirements because of the large number of training
instances. This results not only in large computer memory
usage and long response time but also, very often, in oversensitivity to noise, which degrades algorithmic performance. To tackle such problems, various instance reduction
algorithms have been developed that remove noisy and
redundant patterns. Inspired by the data seriation
approach of the VAT algorithm, Nikolaidis et al. [165] introduced a new approach: instance seriation for prototype
abstraction, which is a data-condensation method that
generates a new set of prototypes. This helps reduce the
storage requirements of instance-based algorithms and
make them resistant to data noise.
Optimization
For continuous multiobjective optimization problems, there
are an infinite number of Pareto optimal solutions. However, many multiobjective evolutionary algorithms [166] fail to
find and preserve all of the multimodal solutions in the



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