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Data Mining using WEKA Geoff Holmes Department of Computer Science, University of Waikato, New Zealand WEKA project and team Data Mining process Data format Preprocessing Classification Regression Clustering Associations Attribute selection Visualization Performing experiments New Directions Conclusion Waikato Environment for Knowledge Analysis Copyright: Martin Kramer ([email protected]) • PGSF/NERF project been going since 1994. • 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 2 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. 3 Data mining process Selected data Preprocessed data Select Preprocess Transform Transformed data Mine Extracted information Assimilated knowledge Analyze & Assimilate 4 Data mining software Commercial packages (Cost ? X 106 dollars) IBM Intelligent Miner SAS Enterprise Miner Clementine WEKA (Free = GPL licence!) Java => Multi-platform Open source – means you get source code 5 Data format Outlook Temperature Humidity Windy Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild Normal False Yes … … … … … 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 6 Data complications Volume of data – sampling; essential attributes Missing data Inaccurate data Data filtering Data aggregation 7 WEKA’s ARFF format % % ARFF file for weather data with some numeric features % @relation weather @attribute @attribute @attribute @attribute @attribute outlook {sunny, overcast, rainy} temperature numeric humidity numeric windy {true, false} play? {yes, no} @data sunny, 85, 85, false, no sunny, 80, 90, true, no overcast, 83, 86, false, yes ... 8 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? 9 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 10 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! 11 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 Input Model takes new instance as input Outputs prediction Model Prediction 12 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 13 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, … 14 Explorer: pre-processing 15 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, … 16 Explorer: classification 17 Explorer: classification 18 Explorer: classification 19 Explorer: classification 20 Explorer: classification 21 Explorer: classification 22 Explorer: classification 23 Explorer: classification 24 Explorer: classification/regression 25 Explorer: classification 26 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 27 Explorer: clustering 28 Explorer: clustering 29 Explorer: clustering 30 Explorer: clustering 31 Finding associations WEKA contains an implementation of the Apriori algorithm for learning association rules Allows you to identify statistical dependencies between groups of attributes: Works only with discrete data 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 32 Explorer: association rules 33 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 34 Explorer: attribute selection 35 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 36 Explorer: data visualization 37 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! 38 Experimenter: setting it up 39 Experimenter: running it 40 Experimenter: analysis 41 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 42 Next Generation Weka: Knowledge flow GUI 43 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 http://www.cs.waikato.ac.nz/~ml Weka provides the perfect environment for ongoing research in data mining. 44