Chapters 1–5 are devoted to basic topics and ideas that are shared by all classical clustering methods.Devising a useful solution that discovers cluster structure in a real problem requires some knowledge abouta lot of things that might not be obvious to a casual user. There are some specific methods and algorithmsin Part I, but they reside here mainly as examples which illustrate underlying notions about a topic that isimportant in the context of clustering. With this in mind, here is a preview of Part I.Chapter 1 discusses the basic ideas underlying cluster analysis from the human point of view (HPOV).I firmly believe that most users of computational clustering algorithms (more or less) transfer what theythink they know about clusters of real objects to computational counterparts built from data processing. Butthere are some very important differences between the human and computer points of view, and Chapter 1paves the way for understanding them.Chapter 2 discusses the foundations of fuzzy sets and models. The material in this chapter is quite basicand is needed to understand fuzzy clustering, which is covered in some detail in Chapter 6. Fuzzy modelsare compared to the other longstanding approach to modeling uncertainty – viz., probability theory. Don’tlook for a winner – they are both winners!Chapter 3 is a counterpoint to Chapter 1 that discusses some ways to think about what it means to askcomputers to look for clusters in numerical data; in short, how to think about clusters from the computerpoint of view (CPOV). Understanding the difference between the human and computer points of view is agreat help when you are trying to make a computer find clusters.Chapter 4 defines and illustrates the three main topics of batch (as opposed to streaming) cluster analysis:(tendency assessment, partitioning, and cluster validity). There is also a subsection about visual assessmentof clustering tendency. Trying to understand what your data might contain before you look…