Clustering and Bayesian Networks

Abstract

The goal of this chapter is to present an outline of clustering and Bayesian schemes used in data mining, machine learning communities. Standardized data into sensible groups is the preeminent mode of understanding as well as learning. A cluster constitutes a set regarding entities that are alike and entities from different clusters are not alike. Representing data by fewer clusters inevitably loses certain fine important information but achieves better simplification. There is no training stage in clustering; mostly, it's used when the classes are not well-known. Bayesian network is one of the best classification methods and is frequently used. Generally, Bayesian network is a form of graphical probabilistic representation model that consists of a set of interconnected nodes, where each node represents a variable, and inter-link connection represents a causal relationship of those variables. Belief networks are graph symbolized models that successfully model familiarity via transmitting probabilistic information to a variety of assumptions.