Multiway and multiset data analysis

The field of multiway data analysis is maturing and deserves a
second special issue after the first one (Journal of Chemometrics, Vol.
23, Nur. 7 and 8). Also it is increasingly being recognized that multiway
methods can be considered a special case of a more general class of
methods, namely multiset or multiblock methods. We therefore
present a set of 16 papers devoted to the topics of multiway and
multiset data analysis. A major part of these papers were presented
and discussed in the TRICAP (ThRee-way methods In Chemistry And
Psychology) meeting held in Vall de Núria - Spain, June 14th to June
18th, 2009 (http://www.tricap2009.info/, see also Chemometrics and
Intelligent Laboratory Systems 99 (2009) 161). This is a special
thematic issue on the topic of multiway and multiset data analysis and
not an ordinary proceedings issue. Authors have been invited to
contribute according to their previously known work in the field and
papers were then submitted to the ordinary peer review system for
their improvement. We have prepared this special issue with the goal
of contributing significantly to the extension of these important topics
in chemometrics and also in other related fields.
Multiset data is a collection of data sets related to one central
question. In most cases, it is not sufficient to analyze the data sets
separately but a simultaneous analysis is required. Multiset data have
a less rigorous structure than multiway data and it is not trivial how to
analyze such data. One of the key notions is shared modes between
the data sets, e.g., when multiple sets of variables are measured for the
same samples. Simultaneous analysis then makes use of this shared
sampling mode. The extension of multiway to multiset data analysis
has also obvious and important implications for the models designed
to investigate them, requiring alternative modelling approaches
complementary to the powerful multilinear models which are already
rather mature. Both theoretical and new applications of multiway and multiset
data analysis methods are presented.