Elementary Cluster Analysis Four Basic Methods That Usually Work Mangey Ram by Mangey Ram 9788770224253, 9788770224246, 8770224250, 8770224242 instant download after payment.

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 about

a lot of things that might not be obvious to a casual user. There are some specific methods and algorithms

in Part I, but they reside here mainly as examples which illustrate underlying notions about a topic that is

important 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 they

think they know about clusters of real objects to computational counterparts built from data processing. But

there are some very important differences between the human and computer points of view, and Chapter 1

paves the way for understanding them.

Chapter 2 discusses the foundations of fuzzy sets and models. The material in this chapter is quite basic

and is needed to understand fuzzy clustering, which is covered in some detail in Chapter 6. Fuzzy models

are compared to the other longstanding approach to modeling uncertainty – viz., probability theory. Don’t

look 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 ask

computers to look for clusters in numerical data; in short, how to think about clusters from the computer

point of view (CPOV). Understanding the difference between the human and computer points of view is a

great 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 assessment

of clustering tendency. Trying to understand what your data might contain before you look