Data Mining in Agriculture by Antonio Mucherino

By Antonio Mucherino

Data Mining in Agriculture represents a accomplished attempt to supply graduate scholars and researchers with an analytical textual content on info mining ideas utilized to agriculture and environmental similar fields. This booklet offers either theoretical and sensible insights with a spotlight on providing the context of every information mining approach quite intuitively with abundant concrete examples represented graphically and with algorithms written in MATLAB®.

Examples and routines with ideas are supplied on the finish of every bankruptcy to facilitate the comprehension of the fabric. for every facts mining procedure defined within the booklet editions and enhancements of the elemental set of rules also are given.

Also by way of P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its functions' vol. 25, ©2009.

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However, researchers commonly use such methods. They first seek to find out the method which is the best fit for their problem. This decision may result in trading off the quality of the solution with speed or ease of implementation. For high-quality solutions, modeling issues may usually become more complex, requiring additional programming skills and powerful computational environments [174]. Once a global optimization problem has been formulated, the usual approach is to attempt to solve it by using one of the many methods for optimization.

Finally, Chapter 7 is focused on biclustering techniques. Biclustering has been recently proposed and it is very efficient in some kind of applications. There are no applications in agriculture yet which use this method. However, a chapter in this book is devoted to it for completeness, and an application in the field of biology is presented. Chapters have a common structure. The first sections are dedicated to the data mining techniques. Basic ideas are given, as well as variants and improvements of the technique proposed over time.

6 General structure of the book 21 them by using little code. Codes in MATLAB are provided for both techniques. They are very simple and may not work in some kinds of situations. Our aim is to keep the simplicity, however the reader could even modify such codes for solving particular problems. Artificial neural network and support vector machines are much more complex. Therefore, various software implementing such techniques are presented and examples on how to use them are discussed. At the end of each chapter, a section devoted to exercises is given.

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