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data mining with weka application

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It’s a data mining/machine learning tool developed by Department of Computer Science, University of Waikato, New Zealand. Weka is a collection of machine learning algorithms for data mining tasks. Weka is open source software issued under the GNU General Public License

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WEKA Training Software
Content  What is WEKA?  Data set in WEKA  The Explorer :  Preprocess data  Classification  Clustering  Association Rules  Attribute Selection  Data Visualization  References and Resources 2
What is WEKA?  Waikato Environment for Knowledge Analysis  It’s a data mining/machine learning tool developed by Department of  Computer Science, University of Waikato, New Zealand.  Weka is a collection of machine learning algorithms for data mining tasks.  Weka is open source software issued under the GNU General Public License. 3
Main Features  49 data preprocessing tools  76 classification/regression algorithms  8 clustering algorithms  3 algorithms for finding association rules  15 attribute/subset evaluators + 10 search algorithms for feature selection 4
Main GUI  Three graphical user interfaces  “The Explorer” (exploratory data analysis)  “The Experimenter” (experimental environment)  “The KnowledgeFlow” (new process model inspired interface)  Simple CLI (Command prompt) Offers some functionality not available via the GUI1 5 1.Graphic User Interface
Datasets in Weka  Each entry in a dataset is an instance of the java class:  weka.core.Instance  Each instance consists of a number of attributes  Nominal: one of a predefined list of values e.g. red, green, blue  Numeric: A real or integer number  String: Enclosed in “double quotes”  Date  Relational
ARFF File Weka wants its input data in ARFF format. @relation weather @attribute outlook { sunny, overcast, rainy } @attribute temperature numeric @attribute humidity numeric @attribute windy { TRUE, FALSE } @attribute play { yes, no } @data sunny, 85, 85, FALSE, no sunny, 80, 90, TRUE, no overcast, 83, 86, FALSE, yes rainy, 70, 96, FALSE, yes rainy, 68, 80, FALSE, yes rainy, 65, 70, TRUE, no overcast, 64, 65, TRUE, yes sunny, 72, 95, FALSE, no sunny, 69, 70, FALSE, yes rainy, 75, 80, FALSE, yes sunny, 75, 70, TRUE, yes overcast, 72, 90, TRUE, yes overcast, 81, 75, FALSE, yes rainy, 71, 91, TRUE, no
WEKA : Explorer  Preprocess : Choose and modify the data being acted on.  Classify: Train and test learning schemes that classify or perform regression.  Cluster : Learn clusters for the data.  Associate : Learn association rules for the data.  Select attributes : Select the most relevant attributes in the data.  Visualize : View an interactive 2D plot of the data.
07/11/17 10 analyze load filter
classifiers • Algorithm for Decision Tree Induction OutLookOutLook overcastovercast HumidityHumidity = Sunny= Sunny = Rainy= Rainy nono yesyes yesyes = Normal= Normal= High= High example WindyWindy yesyesnono =False=False=True=True
Explorer : clustering data 14 Scheme:weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100 Relation: weather.symbolic Instances: 14 Attributes: 5 outlook temperature humidity windy play Test mode : evaluate on training data Cluster Attribute 0 (1) outlook sunny 6 overcast 5 rainy 6 [total] 17 temperature hot 5 mild 7 cool 5 [total] 17 Cluster humidity high 8 normal 8 [total] 16 windy TRUE 7 FALSE 9 [total] 16 play yes 10 no 6 [total] 16
Associate Generated sets of large item sets Best rules found
Panel that can be used to investigate which (subsets of) attributes are the most predictive ones Attribute selection methods contain two parts: A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking An evaluation method: correlation-based, wrapper, information gain, chi-squared, … Very flexible: WEKA allows (almost) arbitrary combinations of these two Explorer : attribute selection Explorer : data visualization  Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem  WEKA can visualize single attributes (1-d) and pairs of attributes (2-d) To do: rotating 3-d visualizations (Xgobi-style)  Color-coded class values  “Jitter” option to deal with nominal attributes (and to detect “hidden” data points)  “Zoom-in” function
 References:  WEKA website: http://www.cs.waikato.ac.nz/~ml/weka/index.html  WEKA Tutorial:  Machine Learning with WEKA: A presentation demonstrating all graphical user interfaces (GUI) in Weka.  A presentation which explains how to use Weka for exploratory data mining.  WEKA Data Mining Book:  Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)  WEKA Wiki: http://weka.sourceforge.net/wiki/index.php/Main_Page  Others:  Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd ed.
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data mining with weka application

  • 2. Content  What is WEKA?  Data set in WEKA  The Explorer :  Preprocess data  Classification  Clustering  Association Rules  Attribute Selection  Data Visualization  References and Resources 2
  • 3. What is WEKA?  Waikato Environment for Knowledge Analysis  It’s a data mining/machine learning tool developed by Department of  Computer Science, University of Waikato, New Zealand.  Weka is a collection of machine learning algorithms for data mining tasks.  Weka is open source software issued under the GNU General Public License. 3
  • 4. Main Features  49 data preprocessing tools  76 classification/regression algorithms  8 clustering algorithms  3 algorithms for finding association rules  15 attribute/subset evaluators + 10 search algorithms for feature selection 4
  • 5. Main GUI  Three graphical user interfaces  “The Explorer” (exploratory data analysis)  “The Experimenter” (experimental environment)  “The KnowledgeFlow” (new process model inspired interface)  Simple CLI (Command prompt) Offers some functionality not available via the GUI1 5 1.Graphic User Interface
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  • 7. Datasets in Weka  Each entry in a dataset is an instance of the java class:  weka.core.Instance  Each instance consists of a number of attributes  Nominal: one of a predefined list of values e.g. red, green, blue  Numeric: A real or integer number  String: Enclosed in “double quotes”  Date  Relational
  • 8. ARFF File Weka wants its input data in ARFF format. @relation weather @attribute outlook { sunny, overcast, rainy } @attribute temperature numeric @attribute humidity numeric @attribute windy { TRUE, FALSE } @attribute play { yes, no } @data sunny, 85, 85, FALSE, no sunny, 80, 90, TRUE, no overcast, 83, 86, FALSE, yes rainy, 70, 96, FALSE, yes rainy, 68, 80, FALSE, yes rainy, 65, 70, TRUE, no overcast, 64, 65, TRUE, yes sunny, 72, 95, FALSE, no sunny, 69, 70, FALSE, yes rainy, 75, 80, FALSE, yes sunny, 75, 70, TRUE, yes overcast, 72, 90, TRUE, yes overcast, 81, 75, FALSE, yes rainy, 71, 91, TRUE, no
  • 9. WEKA : Explorer  Preprocess : Choose and modify the data being acted on.  Classify: Train and test learning schemes that classify or perform regression.  Cluster : Learn clusters for the data.  Associate : Learn association rules for the data.  Select attributes : Select the most relevant attributes in the data.  Visualize : View an interactive 2D plot of the data.
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  • 12. classifiers • Algorithm for Decision Tree Induction OutLookOutLook overcastovercast HumidityHumidity = Sunny= Sunny = Rainy= Rainy nono yesyes yesyes = Normal= Normal= High= High example WindyWindy yesyesnono =False=False=True=True
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  • 14. Explorer : clustering data 14 Scheme:weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100 Relation: weather.symbolic Instances: 14 Attributes: 5 outlook temperature humidity windy play Test mode : evaluate on training data Cluster Attribute 0 (1) outlook sunny 6 overcast 5 rainy 6 [total] 17 temperature hot 5 mild 7 cool 5 [total] 17 Cluster humidity high 8 normal 8 [total] 16 windy TRUE 7 FALSE 9 [total] 16 play yes 10 no 6 [total] 16
  • 15. Associate Generated sets of large item sets Best rules found
  • 16. Panel that can be used to investigate which (subsets of) attributes are the most predictive ones Attribute selection methods contain two parts: A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking An evaluation method: correlation-based, wrapper, information gain, chi-squared, … Very flexible: WEKA allows (almost) arbitrary combinations of these two Explorer : attribute selection Explorer : data visualization  Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem  WEKA can visualize single attributes (1-d) and pairs of attributes (2-d) To do: rotating 3-d visualizations (Xgobi-style)  Color-coded class values  “Jitter” option to deal with nominal attributes (and to detect “hidden” data points)  “Zoom-in” function
  • 17.
  • 18.  References:  WEKA website: http://www.cs.waikato.ac.nz/~ml/weka/index.html  WEKA Tutorial:  Machine Learning with WEKA: A presentation demonstrating all graphical user interfaces (GUI) in Weka.  A presentation which explains how to use Weka for exploratory data mining.  WEKA Data Mining Book:  Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)  WEKA Wiki: http://weka.sourceforge.net/wiki/index.php/Main_Page  Others:  Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd ed.
  • 19. You Have Question ?You Have Question ?