Download E-books Data Mining in Agriculture (Springer Optimization and Its Applications) PDF

By Antonio Mucherino

Data Mining in Agriculture represents a accomplished attempt to supply graduate scholars and researchers with an analytical textual content on facts mining innovations utilized to agriculture and environmental comparable fields. This ebook provides either theoretical and sensible insights with a spotlight on featuring the context of every info mining process fairly intuitively with plentiful concrete examples represented graphically and with algorithms written in MATLAB®.

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23 23 30 36 37 forty forty forty four three Clustering through k-means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . three. 1 the fundamental k-means set of rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . three. 2 editions of the k-means set of rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . three. three Vector quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . forty seven forty seven fifty six sixty two ix x Contents three. four Fuzzy c-means clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . three. five functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . three. five. 1 Prediction of wine fermentation challenge . . . . . . . . . . . . . . . . three. five. 2 Grading approach to apples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . three. 6 Experiments in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . three. 7 routines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . sixty four sixty seven sixty eight seventy one seventy three eighty four k-Nearest Neighbor category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . eighty three four. 1 an easy category rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . eighty three four. 2 decreasing the educational set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . eighty five four. three rushing k-NN up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 four. four purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 four. four. 1 weather forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ninety one four. four. 2 Estimating soil water parameters . . . . . . . . . . . . . . . . . . . . . . . ninety three four. five Experiments in MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ninety six four. 6 routines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 five synthetic Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 five. 1 Multilayer perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 five. 2 education a neural community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 five. three The pruning procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 five. four purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 five. four. 1 Pig cough attractiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 five. four. 2 Sorting apples via watercore . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 five. five software program for neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 five. 6 workouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6 help Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6. 1 Linear classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6. 2 Nonlinear classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6. three Noise and outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 6. four education SVMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . one hundred thirty 6. five purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 6. five. 1 reputation of poultry species . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6. five. 2 Detection of meat and bone meal . . . . . . . . . . . . . . . . . . . . . . . one hundred thirty five 6. 6 MATLAB and LIBSVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 6. 7 routines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 7 Biclustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 7. 1 Clustering in dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 7. 2 constant biclustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 7. three Unsupervised and supervised biclustering . . . . . . . . . . . . . . . . . . . . . . 151 7. four purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 7. four. 1 Biclustering microarray information .

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