Mucherino, Antonio, Papajorgji, Petraq J., Pardalos, Panos M.
2009, XVIII, 274p. 92 illus..
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Suitable for students, researchers and professionals in the classroom or as a self-study
Explores examples in agricultural and environmental fields
Provides Matlab codes to illustrate examples
Includes numerous exercises and some solutions
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®.
Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given.
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.