Department of Computer Science, University of Waikato, New Zealand Geoff Holmes WEKA project and team Data Mining process Data format Preprocessing Classification Regression Clustering Associations Attribute selection Visualization Performing experiments New Directions Conclusion Data Mining using WEKA

2 Waikato Environment for Knowledge Analysis Copyright: Martin Kramer PGSF/NERF project been going since New Java software development from 98 on. Project goals: Develop a state-of-the-art workbench of data mining tools Explore fielded applications Develop new fundamental methods

3 WEKA TEAM Geoff Holmes, Ian Witten, Bernhard Pfahringer, Eibe Frank, Mark Hall, Yong Wang, Remco Bouckaert, Peter Reutemann, Gabi Schmidberger, Dale Fletcher, Tony Smith, Mike Mayo and Richard Kirkby Members on editorial board of MLJ, programme committees for ICML, ECML, KDD, …. Authors of a widely adopted data mining textbook.

4 Data mining process SelectPreprocessTransform MineAnalyze & Assimilate Selected data Preprocessed data Transformed data Extracted information Assimilated knowledge

5 Data mining software Commercial packages (Cost ? X 10 6 dollars)  IBM Intelligent Miner  SAS Enterprise Miner  Clementine WEKA (Free = GPL licence!)  Java => Multi-platform  Open source – means you get source code

6 Data format Rectangular table format (flat file) very common  Most techniques exist to deal with table format Row=instance=individual=data point=case=record Column=attribute=field=variable=characteristic=dimension OutlookTemperatureHumidityWindyPlay SunnyHotHighFalseNo SunnyHotHighTrueNo OvercastHotHighFalseYes RainyMildNormalFalseYes ……………

7 Data complications Volume of data – sampling; essential attributes Missing data Inaccurate data Data filtering Data aggregation

8 WEKA’s ARFF format % % ARFF file for weather data with some numeric features outlook {sunny, overcast, temperature humidity windy {true, play? {yes, sunny, 85, 85, false, no sunny, 80, 90, true, no overcast, 83, 86, false, yes...

9 Attribute types ARFF supports numeric and nominal attributes Interpretation depends on learning scheme  Numeric attributes are interpreted as - ordinal scales if less-than and greater-than are used - ratio scales if distance calculations are performed (normalization/standardization may be required)  Instance-based schemes define distance between nominal values (0 if values are equal, 1 otherwise) Integers: nominal, ordinal, or ratio scale?

10 Missing values Frequently indicated by out-of-range entries  Types: unknown, unrecorded, irrelevant  Reasons: malfunctioning equipment, changes in experimental design, collation of different datasets, measurement not possible Missing value may have significance in itself (e.g. missing test in a medical examination)  Most schemes assume that is not the case  “missing” may need to be coded as additional value

11 Getting to know the data Simple visualization tools are very useful for identifying problems  Nominal attributes: histograms (Distribution consistent with background knowledge?)  Numeric attributes: graphs (Any obvious outliers?) 2-D and 3-D visualizations show dependencies Domain experts need to be consulted Too much data to inspect? Take a sample!

12 Learning and using a model Learning  Learning algorithm takes instances of concept as input  Produces a structural description (model) as output Input: concept to learn Learning algorithm Model Prediction  Model takes new instance as input  Outputs prediction Input Model Prediction

13 Structural descriptions (models) Some models are better than others  Accuracy  Understandability Models range from “easy to understand” to virtually incomprehensible  Decision trees  Rule induction  Regression models  Neural networks Easier Harder

14 Pre-processing the data Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary Data can also be read from a URL or from SQL databases using JDBC Pre-processing tools in WEKA are called “filters” WEKA contains filters for:  Discretization, normalization, resampling, attribute selection, attribute combination, …

15 Explorer: pre-processing

16 Building classification models “Classifiers” in WEKA are models for predicting nominal or numeric quantities Implemented schemes include:  Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, … “Meta”-classifiers include:  Bagging, boosting, stacking, error-correcting output codes, data cleansing, …

17 Explorer: classification

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25 Explorer: classification/regression

26 Explorer: classification

27 Clustering data WEKA contains “clusterers” for finding groups of instances in a datasets Implemented schemes are:  k-Means, EM, Cobweb Coming soon: x-means Clusters can be visualized and compared to “true” clusters (if given) Evaluation based on loglikelihood if clustering scheme produces a probability distribution

28 Explorer: clustering

29 Explorer: clustering

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31 Explorer: clustering

32 Finding associations WEKA contains an implementation of the Apriori algorithm for learning association rules  Works only with discrete data Allows you to identify statistical dependencies between groups of attributes:  milk, butter  bread, eggs (with confidence 0.9 and support 2000) Apriori can compute all rules that have a given minimum support and exceed a given confidence

33 Explorer: association rules

34 Attribute selection Separate panel allows you 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, race search, ranking  An evaluation method: correlation-based, wrapper, information gain, chi-squared, PCA, … Very flexible: WEKA allows (almost) arbitrary combinations of these two

35 Explorer: attribute selection

36 Data visualization Visualization is 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

37 Explorer: data visualization

38 Performing experiments The Experimenter makes it easy to compare the performance of different learning schemes applied to the same data. Designed for nominal and numeric class problems Results can be written into file or database Evaluation options: cross-validation, learning curve, hold-out Can also iterate over different parameter settings Significance-testing built in!

39 Experimenter: setting it up

40 Experimenter: running it

41 Experimenter: analysis

42 New Directions for Weka New user interface based on work flows New data mining techniques  PACE regression  Bayesian Networks  Logistic option trees New frameworks for very large data sources (MOA) New applications in the agricultural sector  Matchmaker for RPBC Ltd  Pest control for kiwifruit management  Crop forecasting

43 Next Generation Weka: Knowledge flow GUI

44 Conclusions Weka is a comprehensive suite of Java programs united under a common interface to permit exploration and experimentation on datasets using state-of-the-art techniques. The software is available under the GPL from Weka provides the perfect environment for ongoing research in data mining.