The advent of the high-speed computer with its enormous storage capabilities enabled statisticians as well as researchers from the different topics of life sciences to apply mul­ tivariate statistical procedures to large data sets to explore their structures. More and more, methods of graphical representation and data analysis are used for investigations. These methods belong to a topic of growing popUlarity, known as "exploratory data analysis" or EDA. In many applications, there is reason to believe that a set of objects can be clus­ tered into subgroups that differ in meaningful ways. Extensive data sets, for example, are stored in clinical cancer registers. In large data sets like these, nobody would ex­ pect the objects to be homogeneous. The most commonly used terms for the class of procedures that seek to separate the component data into groups are "cluster analysis" or "numerical taxonomy". The origins of cluster analysis can be found in biology and anthropology at the beginning of the century. The first systematic investigations in cluster analysis are those of K. Pearson in 1894. The search for classifications or ty­ pologies of objects or persons, however, is indigenous not only to biology but to a wide variety of disciplines. Thus, in recent years, a growing interest in classification and related areas has taken place. Today, we see applications of cluster analysis not only to. biology but also to such diverse areas as psychology, regional analysis, marketing research, chemistry, archaeology and medicine.

What is space? Is there space when there are objects to occupy it or is there space only when there are no objects to occupy it? Can there be space without objects? These are old philosophical questions that concern the ontology of space in the philosophical sense of ‘ontology’ – what is the nature of space? Cognitive science in general and arti?cial intelligence in particular are less c- cerned with the nature of things than with their mental conceptualizations. In spatial cognition research we address questions like What do we know about space? How is space represented? What are the representational entities? What are the rep- sentational structures? Answers to these questions are described in what is called ontologies in arti?cial intelligence. Different tasks require different knowledge, and different representations of knowledge facilitate different ways of solving problems. In this book, Jan Oliver Wallgrün develops and investigates representational structures to support tasks of autonomous mobile robots, from the acquisition of knowledge to the use of this knowledge for navigation. The research presented is concerned with the robot mapping problem, the pr- lem of building a spatial representation of an environment that is perceived by s- sors that only provide incomplete and uncertain information; this information usually needs to be related to other imprecise or uncertain information. The routes a robot can take can be abstractly described in terms of graphs where alternative routes are represented by alternative branches in these route graphs.

To most graph theorists there are two outstanding landmarks in the history of their subject. One is Euler's solution of the Konigsberg Bridges Problem, dated 1736, and the other is the appearance of Denes Konig's textbook in 1936. "From Konigsberg to Konig's book" sings the poetess, "So runs the graphic tale . . . " [10]. There were earlier books that took note of graph theory. Veb­ len's Analysis Situs, published in 1931, is about general combinato­ rial topology. But its first two chapters, on "Linear graphs" and "Two-Dimensional Complexes", are almost exclusively concerned with the territory still explored by graph theorists. Rouse Ball's Mathematical Recreations and Essays told, usually without proofs, of the major graph-theoretical advances ofthe nineteenth century, of the Five Colour Theorem, of Petersen's Theorem on I-factors, and of Cayley's enumerations of trees. It was Rouse Ball's book that kindled my own graph-theoretical enthusiasm. The graph-theoretical papers of Hassler Whitney, published in 1931-1933, would have made an excellent textbook in English had they been collected and published as such. But the honour of presenting Graph Theory to the mathe­ matical world as a subject in its own right, with its own textbook, belongs to Denes Konig. Low was the prestige of Graph Theory in the Dirty Thirties. It is still remembered, with resentment now shading into amuse­ ment, how one mathematician scorned it as "The slums of Topol­ ogy".

This book presents a computer-aided approach to the design of mechatronic systems. Its subject is an integrated modeling and simulation in a visual computer environment. Since the first edition, the simulation software changed enormously, became more user-friendly and easier to use. Therefore, a second edition became necessary taking these improvements into account.

All recordings previously unreleased. Grapefruit were a London-based British band of the late 1960s, brought together by Terry Doran of Apple Publishing, a music publishing company started and owned by the Beatles. Their brand of music was a typical late 1960s blend of rock, which they often fused with psychedelic effects such as phasers and vocoders, or classical arrangements.

Arc of a Diver is the second solo studio album by singer/multi-instrumentalist Steve Winwood. Released in 1980, Winwood played all of the instruments on the album. Featuring his first solo hit, "While You See a Chance" (which peaked at number 7 on the Billboard Hot 100 in the United States), this was Winwood's breakthrough album as a solo artist. It peaked at number 3 on the Billboard 200 album chart, establishing him as a commercially viable act. The album was included in the book 1001 Albums You Must Hear Before You Die, and was voted number 455 in the third edition (2000) of Colin Larkin's All Time Top 1000 Albums.

Recent technology involves large-scale physical or engineering systems consisting of thousands of interconnected elementary units. This monograph illustrates how engineering problems can be solved using the recent results of combinatorial mathematics through appropriate mathematical modeling. The structural solvability of a system of linear or nonlinear equations as well as the structural controllability of a linear time-invariant dynamical system are treated by means of graphs and matroids. Special emphasis is laid on the importance of relevant physical observations to successful mathematical modelings. The reader will become acquainted with the concepts of matroid theory and its corresponding matroid theoretical approach. This book is of interest to graduate students and researchers.