Literature

Apart from Data Mining: Practical Machine Learning Tools and Techniques, there are several other books with material on Weka:

• Jason Bell (2020) Machine Learning: Hands-On for Developers and Technical Professionals, Second Edition, Wiley.

• Richard J. Roiger (2020) Just Enough R! An Interactive Approach to Machine Learning and Analytics, CRC Press.

• Parteek Bhatia (2019) Data Mining and Data Warehousing Principles and Practical Techniques, Cambridge University Press.

• Mark Wickham (2018) Practical Java Machine Learning Projects with Google Cloud Platform and Amazon Web Services, APress.

• AshishSingh Bhatia, Bostjan Kaluza (2018) Machine Learning in Java - Second Edition, Packt Publishing.

• Richard J. Roiger (2016) Data Mining: A Tutorial-Based Primer, CRC Press.

• Mei Yu Yuan (2016) Data Mining and Machine Learning: WEKA Technology and Practice, Tsinghua University Press (in Chinese).

• Jürgen Cleve, Uwe Lämmel (2016) Data Mining, De Gruyter (in German).

• Eric Rochester (2015) Clojure Data Analysis Cookbook - Second Edition, Packt Publishing.

• Boštjan Kaluža (2013) Instant Weka How-to, Packt Publishing.

• Hongbo Du (2010) Data Mining Techniques and Applications, Cengage Learning.

• A book explaining why Weka won't learn (discovered by Stuart Inglis).