Author Description

Decision trees are simple but powerful and popular approaches in analyzing multiple variables data. They are applied in data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. Decision trees supplement, complement, and substitute traditional statistical methods such as multiple regressions analysis, as well as data mining methods such as neural networks. Decision trees are generated by applying various algorithms that identify various ways of splitting a data set into branch-like structures. Decision tree was part of decision theory and statistics. It is now became highly effective tools in areas such as data mining, data analytics, text mining, information extraction, machine learning, pattern recognition etc. Decision tree models include such concepts as nodes, branches, splitting criteria, terminal values, strategy, payoff distribution, certain equivalent, and the rollback method. This book invites readers to explore the many applications of decision tree models, among which are comparisons of models for same set of empirical evidences, and real life data from different domains of business such as market basket analysis, credit scoring, and e-mail marketing.