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"There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “Data mining : foundations and intelligent paradigms : volume 2 : core topics including statistical, time-series and Bayesian analysis” introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field."

"Focuses on data mining theory and applications. This title intends to introduce some of the developments to a broad audience of both specialists and non-specialists in this field."

"Data mining is one of the most rapidly growing research areas in computer science and statistics. Areas of application covered are diverse and include healthcare and finance. We wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field."

"Summary:Equips you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets. This title focuses on important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data."

"Process Mining adalah bidang ilmu yang relatif baru dan masih terus berkembang. Bidang ini menarik dan dibutuhkan dalam berbagai domain karena dapat digunakan untuk menggali informasi tentang proses bisnis dari sekumpulan besar data yang dimiliki perusahaan dalam bentuk event log."

"Part I. Machine Learning Tools and Techniques: 1. What?s iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what?s been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer."