This series covers in their entirety the elements of behaviormetrics, a term that
encompasses all quantitative approaches of research to disclose and understand
human behavior in the broadest sense. The term includes the concept, theory,
model, algorithm, method, and application of quantitative approaches from
theoretical or conceptual studies to empirical or practical application studies to
comprehend human behavior. The Behaviormetrics series deals with a wide range
of topics of data analysis and of developing new models, algorithms, and methods
to analyze these data.
The characteristics featured in the series have four aspects. The first is the variety
of the methods utilized in data analysis and a newly developed method that includes
not only standard or general statistical methods or psychometric methods
traditionally used in data analysis, but also includes cluster analysis, multidimen-
sional scaling, machine learning, corresponding analysis, biplot, network analysis
and graph theory, conjoint measurement, biclustering, visualization, and data and
web mining. The second aspect is the variety of types of data including ranking,
categorical, preference, functional, angle, contextual, nominal, multi-mode
multi-way, contextual, continuous, discrete, high-dimensional, and sparse data.
The third comprises the varied procedures by which the data are collected: by
survey, experiment, sensor devices, and purchase records, and other means. The
fourth aspect of the Behaviormetrics series is the diversity of fields from which the
data are derived, including marketing and consumer behavior, sociology, psychol-
ogy, education, archaeology, medicine, economics, political and policy science,
cognitive science, public administration, pharmacy, engineering, urban planning,
agriculture and forestry science, and brain science.
In essence, the purpose of this series is to describe the new horizons opening up
in behaviormetrics — approaches to understanding and disclosing human behaviors
both in the analyses of diverse data by a wide range of methods and in the
development of new methods to analyze these data.
Editor in Chief
Akinori Okada (Rikkyo University)
Managing Editors
Daniel Baier (University of Bayreuth)
Giuseppe Bove (Roma Tre University)
Takahiro Hoshino (Keio University)
