Data mining in agriculture:
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Dordrecht ; Heidelberg ; London ; New York
Springer
2009
|
| Series: | Springer optimization and its applications
Volume 34 |
| Subjects: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017693554&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Physical Description: | xviii, 272 Seiten Illustrationen, Diagramme |
| ISBN: | 9780387886145 9780387886152 |
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| 100 | 1 | |a Mucherino, Antonio |0 (DE-588)1052830110 |4 aut | |
| 245 | 1 | 0 | |a Data mining in agriculture |c by Antonio Mucherino ; University of Florida, Gainesville, FL, USA ; Petraq J. Papajorgji ; University of Florida, Gainesville, FL, USA ; Panos M. Pardalos ; University of Florida, Gainesville, FL, USA |
| 264 | 1 | |a Dordrecht ; Heidelberg ; London ; New York |b Springer |c 2009 | |
| 300 | |a xviii, 272 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Springer optimization and its applications |v Volume 34 | |
| 650 | 4 | |a Landwirtschaft | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Data mining |x Mathematics | |
| 650 | 4 | |a Information storage and retrieval systems |x Agriculture | |
| 650 | 0 | 7 | |a Umweltwissenschaften |0 (DE-588)4137364-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
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| 689 | 0 | 2 | |a Umweltwissenschaften |0 (DE-588)4137364-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Papajorgji, Petraq J. |d 1951- |0 (DE-588)1191115488 |4 aut | |
| 700 | 1 | |a Pardalos, Panos M. |d 1954- |0 (DE-588)115385827 |4 aut | |
| 830 | 0 | |a Springer optimization and its applications |v Volume 34 |w (DE-604)BV021746093 |9 34 | |
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Record in the Search Index
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| adam_text | Titel: Data Mining in Agriculture
Autor: Mucherino, Antonio
Jahr: 2009
Contents
Preface................................................ s,,
List of Figures ..................................................... x,,,
I Introduction to Data Mining .................................... 1
1.1 Why data mining?.......................................... I
1 2 Data mining techniques ......................................1
1.2.) A brief overview..................................... 3
1.2.2 Data representation................................... 6
13 General applications of data mining........................... 10
1.3.1 Data mining for studying brain dynamics................ 11
1.3.2 Data mining in telecommunications..................... 12
1.3.3 Mining market data .................................. 13
14 Data mining and optimization................................ 14
1.4.1 The simulated annealing algorithm ..................... 17
1.5 Data mining and agriculture.................................. ! 9
1.6 General structure of the book................................. 20
2 Statistical Based Approaches .................................... 23
2.1 Principal component analysis................................. 23
2.2 Interpolation and regression.................................. 30
2.3 Applications............................................... 36
2.3.1 Checking chicken breast quality........................ 37
2.3.2 Effects of energy use in agriculture ..................... 40
2.4 Experiments in MATLAB®.................................. 40
2.5 Exercises ................................................. ^4
3 Clustering by A-means.......................................... 47
3. i The basic it-means algorithm................................. 47
3.2 Variants of the it-means algorithm............................. -^
3.3 Vector quantization......................................... *¦
Contents
3.4 Fuzzy omeans clustering.................................... 64
3.5 Applications............................................... 67
3.5.1 Prediction of wine fermentation problem ................ 68
3.5.2 Grading method of apples............................. 71
3.6 Experiments in MATLAB ................................... 73
3.7 Exercises ................................................. 80
£-Nearest Neighbor Classification................................ 83
4.1 A simple classification rule................................... 83
4.2 Reducing the training set.................................... 85
4.3 Speeding k-NN up.......................................... 88
4.4 Applications............................................... 89
4.4.1 Climate forecasting .................................. 91
4.4.2 Estimating soil water parameters....................... 93
4.5 Experiments in MATLAB ................................... 96
4.6 Exercises .................................................103
Artificial Neural Networks......................................107
5.1 Multilayer perceptron.......................................107
5.2 Training a neural network....................................Ill
5.3 The pruning process........................................113
5.4 Applications...............................................114
5.4.1 Pig cough recognition ................................116
5.4.2 Sorting apples by watercore...........................118
5.5 Software for neural networks.................................121
5.6 Exercises .................................................122
Support Vector Machines.......................................123
6.1 Linear classifiers...........................................123
6.2 Nonlinear classifiers........................................126
6.3 Noise and outliers..........................................129
6.4 Training SVMs ............................................130
6.5 Applications..........................,....................131
6.5.1 Recognition of bird species............................133
6.5.2 Detection of meat and bone meal.......................135
6.6 MATLAB and LIBSVM.....................................136
6.7 Exercises .................................................139
Biclustering...................................................143
7.1 Clustering in two dimensions.................................143
7.2 Consistent biclustering......................................148
7.3 Unsupervised and supervised biclustering......................151
7.4 Applications...............................................153
7.4.1 Biclustering microarray data...........................153
7.4.2 Biclustering in agriculture.............................155
7.5 Exercises .................................................159
Contents xi
8 Validation.....................................................161
8.1 Validating data mining techniques.............................161
8.2 Test set method............................................163
8.2.1 An example in MATLAB .............................163
8.3 Leave-one-out method......................................166
8.3.1 An example in MATLAB .............................166
8.4 A-fold method .............................................168
8.4.1 An example in MATLAB .............................170
9 Data Mining in a Parallel Environment...........................173
9.1 Parallel computing .........................................173
9.2 A simple parallel algorithm ..................................176
9.3 Some data mining techniques in parallel .......................177
9.3.1 £-means............................................178
9.3.2 k-NN..............................................179
9.3.3 ANNs..............................................181
9.3.4 SVMs..............................................182
9.4 Parallel computing and agriculture............................184
10 Solutions to Exercises...........................................185
10.1 Problems of Chapter 2 ......................................185
10.2 Problems of Chapter 3 ......................................191
10.3 Problems of Chapter 4 ......................................200
10.4 Problems of Chapter 5 ......................................204
10.5 Problems of Chapter 6 ......................................211
10.6 Problems of Chapter 7 ......................................216
Appendix A: The MATLAB Environment.............................219
A. 1 Basic concepts.............................................219
A.2 Graphic functions..........................................224
A.3 Writing a MATLAB function.................................228
Appendix B: An Application in C.....................................231
B.I /i-means in C..............................................231
B.2 Reading data from a file.....................................238
B.3 An example of main function.................................241
B.4 Generating random data.....................................244
B.5 Running the applications ....................................247
References.........................................................253
Glossary ..........................................................265
Index .............................................................269
List of Figures
1.1 A schematic representation of the classification of the data mining
techniques discussed in this book.............................. 5
1.2 The codes that can be used for representing a DNA sequence...... 8
1.3 Three representations for protein molecules. From left to right: the
full-atom representation of the whole protein, the representation
of the atoms of the backbone only, and the representation through
the torsion angles t and ^................................... 10
1.4 The simulated annealing algorithm............................ 19
2.1 A possible transformation on aligned points: (a) the points are
in their original locations; (b) the points are rotated so that the
variability of their v component is zero......................... 25
2.2 A possible transformation on quasi-aligned points: (a) the points
are in their original locations: (b) the points after the transformation. 26
2.3 A transformation on a set of points obtained by applying PCA.
The circles indicate the original set of points.................... 29
2.4 Interpolation of 10 points by a join-the-dots function............. 31
2.5 Interpolation of 10 points by the Newton polynomial............. 33
2.6 Interpolation of 10 points by a cubic spline..................... 34
2.7 Linear regression of 10 points on a plane....................... 35
2.8 Quadratic regression of 10 points on a plane.................... 36
2.9 Average and standard deviations for all the parameters used for
evaluating the chicken breast quality. Data from [ 156]............ 39
2.10 The PCA method applied in MATLAB® to a random set of points
lying on the line y — x...................................... 41
2. i 1 The figure generated if the MATLAB instructions in Figure 2.10
are executed............................................... 42
2.12 A sequence of instructions for drawing interpolating functions in
MATLAB................................................. 42
iv List ot Figures
2.13 Two figures generated by MATLAB: (a) the instructions in Figure
2.12 are executed: (b) the instructions in Figure 2.14 are executed. . 43
2.14 A sequence of instructions for drawing interpolating and regression
functions in MATLAB....................................... 44
3.1 A partition in clusters of a set of points. Points are marked by
the same symbol if they belong to the same cluster. The two big
circles represent the centers of the two clusters.................. 49
3.2 The Lloyd s or fc-means algorithm............................. 50
3.3 Two possible partitions in clusters considered by the A means
algorithm, (a) The first partition is randomly generated; (b) the
second partition is obtained after one iteration of the algorithm..... 51
3.4 Two Voronoi diagrams in two easy cases: (a) the set contains only
2 points; (b) the set contains aligned points..................... 53
3.5 A simple procedure for drawing a Voronoi diagram............... 53
3.6 The Voronoi diagram of a random set of points on a plane......... 54
3.7 The A -means algorithm presented in terms of Voronoi diagram..... 54
3.8 Two partitions of a set of points in 5 clusters and Voronoi diagrams
of the centers of the clusters: (a) clusters and cells differ; (b)
clusters and cells provide the same partition..................... 55
3.9 The /i-means algorithm...................................... 56
3.10 The /;-means algorithm presented in terms of Voronoi diagram..... 57
3.11 (a) A partition in 4 clusters in which one cluster is empty (and
therefore there is no cell for representing it); (b) a new cluster is
generated as the algorithm in Figure 3.12 describes............... 59
3.12 The £-means+ algorithm..................................... 60
3.13 The /?-means+ algorithm..................................... 60
3.14 A graphic representation of the compounds considered in datasets
A. B. E and F. A and E are related to data measured within the
three days that the fermentation started; B and F are related to
data measured during the whole fermentation process............. 69
3.15 Classification of wine fermentations by using the fc-means
algorithm with Jt = 5 and by grouping the clusters in 13 groups.
In this analysis the dataset A is used........................... 71
3.16 The MATLAB function generate............................. 74
3.17 Points generated by the MATLAB function generate............ 74
3.18 The MATLAB function centers.............................. 75
3.19 The center (marked by a circle) of the set of points generated by
generate and computed by centers.......................... 76
3.20 The MATLAB function kmeans............................... 77
3.21 The MATLAB function plotp................................ 79
3.22 The partition in clusters obtained by the function kmeans and
displayed by the function plotp.............................. 79
List of Figures xv
3.23 Different partitions in clusters obtained by the function kmeans.
The set of points is generated with different eps values, (a) eps =
0.10, (b) eps = 0.05......................................... 80
3.24 Different partitions in clusters obtained by the function kmeans.
The set of points is generated with different eps values, (a) eps =
0.02, (b) eps = 0............................................ 81
4.1 (a) The 1-NN decision rule: the point ? is assigned to the class on
the left; (b) the k-NN decision rule, with k = 4: the point ? is
assigned to the class on the left as well......................... 84
4.2 The k-NN algorithm........................................ 84
4.3 An algorithm for finding a consistent subset Tcnn of 7;v,v........ 86
4.4 Examples of correct and incorrect classification.................. 86
4.5 An algorithm for finding a reduced subset 7#/vjV of Tnn.......... 87
4.6 The study area of the application of k-NN presented in [97]. The
image is taken from the quoted paper.......................... 90
4.7 The 10 validation sites in Florida and Georgia used to develop the
raw climate model forecasts using statistical correction methods.... 92
4.8 The 10 target combinations of the outputs of FSU-GSM and
FSU-RSM climate models................................... 92
4.9 Graphical representation of k-NN for finding the best match for
a target soil. Image from [ 118 J................................ 95
4.10 The MATLAB function knn.................................. 97
4.11 The training set used with the function knn..................... 98
4.12 The classification of unknown samples performed by the function
knn....................................................... 99
4.13 The MATLAB function condense: first part....................100
4.14 The MATLAB function condense: second part..................101
4.15 (a) The original training set; (b) the corresponding condensed
subset Tcnno btained by the function condense.................102
4.16 The classification of a random set of points performed by knn. The
training set which is actually used is the one in Figure 4.15(b)......103
4.17 The MATLAB function reduce...............................104
4.18 (a) The reduced subset Trnn obtained by the function reduce;
(b) the classification of points performed by knn using the reduced
subset Trnn obtained by the function reduce...................105
5.1 Multilayer perceptron general scheme..........................i 09
5.2 The face and the smile of Mona Lisa recognized by a neural
network system. Image from [200]............................115
5.3 A schematic representation of the test procedure for recording the
sounds issued by pigs. Image from [45]........................117
5.4 The time signal of a pig cough. Image from [45J.................118
5.5 The confusion matrix for a 4-class multilayer perceptron trained
for recognizing pig sounds...................................119
vi Lisl of Figures
5.6 X-ray and classic view of an apple. X-ray can be useful for
detecting internal defects without slicing the fruit................120
6.1 Apples with a short or long stem on a Cartesian system...........124
6.2 (a) Examples of linear classifiers for the apples; (b) the classifier
obtained by applying a SVM.................................124
6.3 An example in which samples cannot be classified by a linear
classifier. .................................................127
6.4 Example of a set of data which is not linearly classifiable in its
original space. It becomes such in a two-dimensional space........128
6.5 Chinese characters recognized by SVMs. Symbols from [63]......132
6.6 The hooked crow (lat. ab.: cornix) can be recognized by an SVM
based on the sounds of birds..................................133
6.7 The structure of the SVM decision tree used for recognizing bird
species. Image from [71].....................................135
6.8 The MATLAB function generate41ibsvm.....................138
6.9 The first rows of file trainset. txt generated by
generate41ibsvm..........................................139
6.10 The DOS commands for training and testing an SVM by SVMLIB. 139
7.1 A microarray...............................................154
7.2 The partition found in biclusters separating the ALL samples and
the AML samples...........................................156
7.3 Tissues from the HuGE Index set of data.......................157
7.4 The partition found in biclusters of the tissues in the HuGE Index
set of data.................................................158
8.1 The test set method for validating a linear regression model.......165
8.2 The test set method for validating a linear regression model. In
this case, a validation set different from the one in Figure 8.1 is used. 166
8.3 The leave-one-out method for validation, (a) The point
(x(i),y(i ) is left out; (b) the point (x(4),y(4)) is left out........168
8.4 The leave-one-out method for validation, (a) The point
(x(7),y(7 ) is left out; (b) the point (x(lO).y(lO)) is left out.....169
8.5 A set of points partitioned in two classes........................171
8.6 The results obtained applying the k-fo d method, (a) Half set is
considered as a training set and the other half as a validation set:
(b) training and validation sets are inverted.....................172
9.1 A graphic scheme of the MIMD computers with distributed and
shared memory.............................................174
9.2 A parallel algorithm for computing the minimum distance between
one sample and a set of samples in parallel......................178
9.3 A parallel algorithm for computing the centers of clusters in parallel. 179
9.4 A parallel version of the fi-means algorithm.....................180
9.5 A parallel version of the k-NN algorithm.......................180
List of Figures xvii
9.6 A parallel version of the training phase of a neural network........182
9.7 The tree scheme used in the parallel training of a SVM...........183
9.8 A parallel version of the training phase of a SVM................183
10.1 A set of points before and after the application of the principal
component analysis.........................................186
10.2 The line which is the solution of Exercise 4.....................187
10.3 The solution of Exercise 7....................................189
10.4 The solution of Exercise 8....................................190
10.5 The solution of Exercise 9....................................190
10.6 The set of points of Exercise 1 plotted with the MATLAB function
plotp. Note that 3 of these points lie on the x or v axis of the
Cartesian system...........................................198
10.7 The training set and the unknown point that represents a possible
solution to Exercise 4.......................................202
10.8 A random set of 200 points partitioned in two clusters............204
10.9 The condensed and reduced set obtained in Exercise 7: (a) the
condensed set corresponding to the set in Figure 10.8: (b) the
reduced set corresponding to the set in Figure 10.8...............205
10.10 The classification of a random set of points by using a training set
of 200 points................................. ..............206
10.11 The classification of a random set of points by using (a) the
condensed set of the set in Figure 10.8; (b) the reduced set of the
set in Figure 10.8...........................................207
10.12 The structure of the network considered in Exercise 1.............208
10.13 The structure of the network considered in Exercise 3.............209
10.14 The structure of the network considered in Exercise 7.............211
10.15 The structure of the network required in Exercise 8...............212
10.16 The classes C+ and C~ in Exercise 3..........................213
A. 1 Points drawn by the MATLAB function plot...................225
A.2 The sine and cosine functions drawn with MATLAB.............227
A.3 The function fun...........................................228
A.4 The graphic of the MATLAB function fun......................229
B. 1 The function hiseans........................................232
B.2 The prototypes of the functions called by hmeans................234
B.3 The function rand_clust....................................235
B.4 The function compute_centers..............................236
B.5 The function f incL_closest..................................237
B.6 The function isstable......................................237
B.7 The function copy_centers..................................238
B.8 An example of input text file..................................239
B.9 The function dimf ile.......................................239
B.10 The function readf ile......................................241
ii List of Figures
B. 11 The function main..........................................242
B. 12 The function main of the application for generating random sets
of data. Part 1..............................................246
B. 13 The function main of the application for generating random sets
of data. Part 2..............................................247
B.14 An example of input text file for the application hmeans...........248
B. 15 The output file provided by the application hmeans when the input
is the file in Figure B.14 and k = 2............................248
B.I 6 An output file containing a set of data generated by the application
generate.................................................249
B.I7 The partition provided by the application generate (column A),
the partition found by hmeans (column B) and the components of
the samples (following columns) in an Excel spreadsheet..........250
|
| any_adam_object | 1 |
| author | Mucherino, Antonio Papajorgji, Petraq J. 1951- Pardalos, Panos M. 1954- |
| author_GND | (DE-588)1052830110 (DE-588)1191115488 (DE-588)115385827 |
| author_facet | Mucherino, Antonio Papajorgji, Petraq J. 1951- Pardalos, Panos M. 1954- |
| author_role | aut aut aut |
| author_sort | Mucherino, Antonio |
| author_variant | a m am p j p pj pjp p m p pm pmp |
| building | Verbundindex |
| bvnumber | BV035638722 |
| callnumber-first | Q - Science |
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| callnumber-sort | QA 276.9 D343 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | SK 990 ST 690 ZA 71000 |
| ctrlnum | (OCoLC)428028035 (DE-599)DNB994589115 |
| dewey-full | 006.312 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.312 |
| dewey-search | 006.312 |
| dewey-sort | 16.312 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau Mathematik |
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| id | DE-604.BV035638722 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:39:30Z |
| institution | BVB |
| isbn | 9780387886145 9780387886152 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017693554 |
| oclc_num | 428028035 |
| open_access_boolean | |
| owner | DE-20 DE-188 DE-83 |
| owner_facet | DE-20 DE-188 DE-83 |
| physical | xviii, 272 Seiten Illustrationen, Diagramme |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Springer |
| record_format | marc |
| series | Springer optimization and its applications |
| series2 | Springer optimization and its applications |
| spellingShingle | Mucherino, Antonio Papajorgji, Petraq J. 1951- Pardalos, Panos M. 1954- Data mining in agriculture Springer optimization and its applications Landwirtschaft Mathematik Data mining Data mining Mathematics Information storage and retrieval systems Agriculture Umweltwissenschaften (DE-588)4137364-9 gnd Data Mining (DE-588)4428654-5 gnd Landwirtschaft (DE-588)4034402-2 gnd |
| subject_GND | (DE-588)4137364-9 (DE-588)4428654-5 (DE-588)4034402-2 |
| title | Data mining in agriculture |
| title_auth | Data mining in agriculture |
| title_exact_search | Data mining in agriculture |
| title_full | Data mining in agriculture by Antonio Mucherino ; University of Florida, Gainesville, FL, USA ; Petraq J. Papajorgji ; University of Florida, Gainesville, FL, USA ; Panos M. Pardalos ; University of Florida, Gainesville, FL, USA |
| title_fullStr | Data mining in agriculture by Antonio Mucherino ; University of Florida, Gainesville, FL, USA ; Petraq J. Papajorgji ; University of Florida, Gainesville, FL, USA ; Panos M. Pardalos ; University of Florida, Gainesville, FL, USA |
| title_full_unstemmed | Data mining in agriculture by Antonio Mucherino ; University of Florida, Gainesville, FL, USA ; Petraq J. Papajorgji ; University of Florida, Gainesville, FL, USA ; Panos M. Pardalos ; University of Florida, Gainesville, FL, USA |
| title_short | Data mining in agriculture |
| title_sort | data mining in agriculture |
| topic | Landwirtschaft Mathematik Data mining Data mining Mathematics Information storage and retrieval systems Agriculture Umweltwissenschaften (DE-588)4137364-9 gnd Data Mining (DE-588)4428654-5 gnd Landwirtschaft (DE-588)4034402-2 gnd |
| topic_facet | Landwirtschaft Mathematik Data mining Data mining Mathematics Information storage and retrieval systems Agriculture Umweltwissenschaften Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017693554&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV021746093 |
| work_keys_str_mv | AT mucherinoantonio datamininginagriculture AT papajorgjipetraqj datamininginagriculture AT pardalospanosm datamininginagriculture |