Chapter 14 deals with clustering algorithms based on cost function optimization，using tools from differential calculus. Hard clustering and fuzzy and possibilisticschemes axe considered, based on various types of cluster representatives, includingpoint representatives, hyperplane representatives, and shell-shaped representatives.In a first course, most of these algorithms are bypassed, and emphasis is given tothe isodata algorithm.
Chapter 15 features a high degree of modularity. It deals with clustering algo-rithms based on different ideas,which cannot be grouped under a single philosophy.Spectral clustering, competitive learning, branch and bound, simulated annealing,and genetic algorithms are some of the schemes treated in this chapter. These arebypassed in a first course.
Chapter 16 deals with the clustering validity stage of a clustering procedure. Itcontains rather advanced concepts and is omitted in a first course. Emphasis is givento the definitions of internal, external, and relative criteria and the random hypothe-ses used in each case. Indices, adopted in the framework of external and internalcriteria, are presented, and examples are provided showing the use of these indices.
Syntactic pattern recognftfon methods are not treated in this book. Syntacticpattern recognition methods differ in philosophy from the methods discussed inthis book and, in general, are applicable to different types of problems. In syntacticpattern recognition, the structure of the patterns is of paramount importance, andpattern recognition is performed on the basis of a set of pattern primitives, a setof rules in the form of a grammar, and a recognizer called automaton. Thus, wewere faced with a dilemma: either to increase the size of the book substantially, orto provide a short overview （which, however, exists in a number of other books）,or to omit it. The last option seemed to be the most sensible choice.
