1. Introduction to WEKA Mark Hall Data Mining WEKA - what is it? WEKA UIs
Integration with Pentaho
Projects based on WEKA
Mark Hall
Pentaho Corporation Suite 340, 5950 Hazeltine National Dr. Orlando, FL 32822, USA

2. Data Mining
A definition: “Extraction of implicit, previously unknown, and potentially useful information from data”
Goal (business oriented): improve marketing, sales, and customer support operations
Who is likely to remain a loyal customer/jump ship?
What products should be marketed to which prospects?
What determines whether a person will respond to a certain offer?
How can I detect potential fraud?
Central idea: historical data contains information that will be useful in the future
Historical patterns provide useful insight and generalize to future situations
Data Mining: algorithms that automatically detect patterns and regularities in data

3. Data Mining Strong patterns can be used to make predictions
Problem 1: a lot of patterns are not interesting
Problem 2: patterns may be inexact (or even completely spurious) if data is garbled or missing
Techniques borrowed from statistics, computer science, machine learning research
Compared to traditional statistics
Statistics is manual, user driven, top-down - formulate a hypothesis, convert hypothesis into database query, test significance of results
Data mining automates the data interrogation
Data-driven, self-organizing, bottom-up
Automatic examination of a large number of hypothesis
Compared to OLAP
OLAP: data summarization - aggregation via addition
# widgets sold in all ZIP codes in the country
Data Mining: ratios, patterns and influences
Factors influencing the sales of the widgets in those ZIP codes
DM can enhance OLAP - suggest dimensions for cube, discretization etc.

4. Data Mining is a Process
Transformed
data
Preprocessed
data
Extracted
information
Assimilated
knowledge
Selected
data
Select Preprocess Transform Mine Analyze &
Assimilate

5. Copyright: Martin Kramer (mkramer@wxs.nl)
What is WEKA?
Copyright: Martin Kramer

9. Hamilton

10. WEKA: The Software Became part of the Pentaho suite in 2006
WEKA (Waikato Environment for Knowledge Analysis)
Funded by the NZ government for more than 10 years
Develop an open-source state-of-the art workbench of data mining tools
Explore fielded applications
Develop new fundamental methods
Became part of the Pentaho suite in 2006
Pentaho Data Mining (PDM)

11. Core Functionality
Support for the whole process of experimental data mining
Preparation of input data
Statistical evaluation of learning schemes
Visualization of input data and the result of learning
Tools and algorithms
69 data pre-processing tools
118 classification/regression algorithms
11 clustering algorithms
18 attribute/subset evaluators + 12 search algorithms for feature selection
6 algorithms for finding association rules
User Interfaces
Explorer - data exploration/visualization, model construction and export, preliminary evaluation
Experimenter - large-scale algorithm comparison with statistical tests for significant differences in performance
KnowledgeFlow - process model view of data mining, export of DM process

12. Architecture Modular, object-oriented architecture
Packages for different types of algorithms: filters, classifiers, clusterers, associations, attribute selection etc.
Sub-packages group components by functionality or purpose
E.g. classifiers.bayes, filters.unsupervised.attribute
Public interface prescribed by abstract base class or interface for all types of algorithms
Algorithms are Java Beans
GUIs use introspection/reflection to dynamically generate editor dialogs at runtime
All components rely to a greater or lesser extent on a “core” top-level package
Classes and data structures for reading data sources; representing instances, sparse instances and attributes; and providing common utility methods
Additional interfaces that indicate extra functionality
Packages containing learning schemes have associated “Evaluation” classes
Routines for performing cross-validation, computing performance metrics, generating ROC curves etc.

13. Explorer

14. Explorer “Preprocess” panel “Classify” panel “Cluster” panel
Load data from various sources (file, SQL database, URL etc.)
Apply pre-processing “filters” to the data
Summary statistics & histograms
“Classify” panel
Apply classification and regression algorithms
Evaluate resulting models
Numerically via statistical estimation
Graphically through visualization (data and model)
“Cluster” panel
Apply clustering algorithms to the data
Visualize the outcome
Clusters that represent density estimates can be evaluated based on the statistical likelihood of the data
“Associate” panel
Learn association rules for market-basket type analysis

15. Explorer “Select attributes” panel “Visualize” panel
Mix and match algorithms for evaluating the utility of attributes and sets of attributes with different search methods
“Visualize” panel
Color-coded scatter plot matrix of the data
Select, enlarge, zoom in etc.

16. Knowledge Flow Define a data mining “process”
Like the Explorer, all of WEKA’s algorithms are available
Data flows through the process from node to node
Accommodates both batch-based processing and data streams
Command line interface to WEKA can also train incremental classifiers on data streams
Fully multi-threaded
Accommodates multiple independent “flows” on the same layout
Knowledge Flow’s Classifier step is multi-threaded
Build models for more than one cross-validation fold in parallel
Binary and XML-based persistence of flow layouts

17. Knowledge Flow

18. Experimenter
Automate the process of determining the best method to use
Is an interactive process in the Explorer or Knowledge Flow
Run classification and regression algorithms on a corpus of data sets
Try different parameter settings
Collect performance statistics
Perform significance tests on the results
Raw output saved to files or databases
Analysis results can be export as text, CSV, Gnuplot, LaTeX or HTML
Advanced users can distribute the processing load across multiple machines

19. Experimenter

20. Extensibility
Plugin mechanisms allow WEKA to be extended without modifying the classes in the WEKA distribution
New tabs can be added to the Explorer
New visualizations can be added to the pop-up menu in the Explorer’s Classify panel
Classifier errors, predictions, trees and graphs
Knowledge Flow - “Plugins” tab
Drop a jar file into $HOME/.knowledgeFlow/plugins/<a plugin>/

21. Standards and Interoperability
Support for PMML import
Regression, general regression and neural network model types
More model types and support for export in future development releases
LibSVM/SVM-Light data format support

22. Integration With Pentaho
Main point of integration is with Pentaho Data Integration (PDI), aka the Kettle project
PDI (Kettle) - streaming, engine-driven ETL tool
PDI can export data in ARFF format
High-volume, low memory consumption
WEKA-specific transformation steps
WekaScoring: score data using a pre-constructed WEKA model (classification, regression or clustering) or PMML model as part of an ETL transformation
KnowledgeFlow: execute arbitrary Knowledge Flow processes as part of an ETL transformation
Can be used to automatically refresh/rebuild a predictive model

23. Scoring as Part of an ETL process

24. Refreshing a predictive model

25. Projects Based on/Using WEKA
Open-source data mining systems
Konstanz Information Miner (KNIME) & RapidMiner provide WEKA plugins
R provides an interface to WEKA through the RWeka package
Scientific workflow environment
Kepler Weka project integrates all of WEKA’s functionality into the Kepler open-source scientific workflow platform
Systems for natural language processing
GATE NLP workbench
Balie - language identification, tokenization, sentence boundary detection, named entity recognition
Kea - automatic keyphrase extraction
Text mining
Judge & IR Utilities - two systems that perform document categorization and clustering

26. Projects Based on/Using WEKA
Knowledge discovery in biology
BioWEKA - extension to WEKA for tasks in biology, bioinformatics and biochemistry
Epitopes Tookit - platform based on WEKA for developing epitope prediction tools
maxdView & Mayday - visualization and analysis of microarray data
Distributed and parallel data mining
Weka-Parallel & GridWeka - distributed cross-validation, scoring and testing
FAEHIM & Weka4WS - make WEKA available as a web service
Connectionist and artificial immune system algorithms
Weka Classification Algorithms Project - several artificial neural networks and artificial immune system based algorithms

27. Impact
Has been downloaded more than 1.5 million times since placed on SourceForge in April 2000
Current download rate of more than 20,000 per month
Large user-base and active community
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