Introduction to Data Mining.- 2 Statistical Methods.- 3 Clustering by k-means.- 4 k-nearest Neighbor Classification.- 5 Artificial Neural Networks.- 6 Support Vector Machines.- 7 Biclustering.- 8 Validation.- 9 An Application in C.- 10 Data Mining in a Parallel Environment.- 11 Solutions of the Exercises.- A. Matlab Environment.- B. C programming language.- C. Message Passing Interface (MPI).- .D. Eigenvalues and Eigenvectors.- References.
Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.
First textbook in data mining in agriculture
Presentation suitable for students, researchers, and professionals, in the classroom or as a self-study
Explores examples in agriculture/environmental fields
Provides Matlab codes to illustrate examples
Includes numerous exercises and some solutions