Data Mining: Concepts, Models, Methods, and Algorithms, 2nd Edition

• Discusses data mining principles and describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, data bases, pattern recognition, machine learning, neural networks, fuzzy logic, and evolutionary computation
• Detailed algorithms are given with necessary explanations, illustrative examples, and questions and exercises for practice at the end of each chapter

Data Mining methodologies have been evolving over time.  New edition includes following new techniques/methodologies

• Support Vector Machines (SVM) – one of the new methodologies, developed based on statistical learning theory, showed large potential for applications in predictive data mining
• Kohonen Maps (Self-organizing Maps –SOM) – one of very applicative neural networks based methodologies for descriptive data mining and multi-dimensional data visualizations
• DBSCAN clustering algorithm – as a representative of an important class of density-based clustering methodologies
• Temporal and Spatial Data Mining – including streaming data analyses is an important trend in data mining recognizing value of time and space information in real world applications
• Web and Text Mining
• Parallel and Distributed Data Mining
• Updates on the older techniques presented in the first edition