Data mining is the process of finding patterns in data.
The beauty of data mining is that it helps to answer questions we didn't know to ask by proactively identifying non-intuitive data patterns through algorithms (e.g., consumers who buy peanut butter are more likely to buy paper towels)..
The Data Mining methods are presented in the second part with the introduction and the very often-used supervised methods.
The third part of the handbook considers 1Introduction to Knowledge Discovery and Data Mining11 the unsupervised methods.
The fourth part is about methods termed soft computing, .
Typical examples include, sup- port vector machines, decision trees, probabilistic summaries, or algebraic function. 1.3Data Mining within the Complete Decision Support System Data Mining methods are becoming part of integrated Information Technology (IT) software packages.
Figure 1.3 illustrates the three tiers of the decision support aspect .
This comprehensive handbook also presents a coherent and unified repository of data science major concepts, theories, methods, trends, challenges and applications.
It covers all the crucial important machine learning methods used in data science.
This handbook aims to serve as the main reference for researchers in the fields of information technology, e-Commerce, information retrieval, data science, machine learning, data mining, databases and statistics as well as advanced level students studying computer science or electrical engineering.
The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application
