This book presents new approaches to data mining and system identification. Algorithms
that can be used for the clustering of data have been overviewed. New techniques and
tools are presented for the clustering, classification, regression and visualization of
complex datasets. Special attention is given to the analysis of historical process data,
tailored algorithms are presented for the data driven modeling of dynamical systems,
determining the model order of nonlinear input-output black box models, and the
segmentation of multivariate time-series. The main methods and techniques are
illustrated through several simulated and real-world applications from data mining and
process engineering practice.
The books is aimed primarily at practitioners, researches, and professionals in statistics,
data mining, business intelligence, and systems engineering, but it is also accessible to
graduate and undergraduate students in applied mathematics, computer science, electrical
and process engineering. Familiarity with the basics of system identification and fuzzy
systems is helpful but not required.
