An Introduction to WEKA Explorer. In part from: Yizhou Sun 2008

Transcription

1 An Introduction to WEKA Explorer In part from: Yizhou Sun 2008

2 What is WEKA? Waikato Environment for Knowledge Analysis It s a data mining/machine learning tool developed by Department of Computer Science,, New Zealand. Weka is also a bird found only on the islands of New Zealand. 2

3 Download and Install WEKA Website: Support multiple platforms (written in java): Windows, Mac OS X and Linux 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- provides users without a graphic interface option the ability to execute commands from a terminal window 5

6 Explorer The Explorer: Preprocess data Classification Clustering Association Rules Attribute Selection Data Visualization References and Resources 6

7 Explorer: 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 an SQL database (using JDBC) Pre-processing tools in WEKA are called filters WEKA contains filters for: Discretization, normalization, resampling, attribute selection, transforming and combining attributes, 7

8 WEKA only deals with flat age sex { female, chest_pain_type { typ_angina, asympt, non_anginal, cholesterol exercise_induced_angina { no, class { present, 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present... 8

9 WEKA only deals with flat age sex { female, chest_pain_type { typ_angina, asympt, non_anginal, cholesterol exercise_induced_angina { no, class { present, 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present... 9

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14 IRIS dataset 5 attributes, one is the classification 3 classes: setosa, versicolor, virginica

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16 Attribute data Min, max and average value of attributes distribution of values :number of items for which: a i = v j a i A,v j V class: distribution of attribute values in the classes

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22 Filtering attributes Once the initial data has been selected and loaded the user can select options for refining the experimental data. The options in the preprocess window include selection of optional filters to apply and the user can select or remove different attributes of the data set as necessary to identify specific information. The user can modify the attribute selection and change the relationship among the different attributes by deselecting different choices from the original data set. There are many different filtering options available within the preprocessing window and the user can select the different options based on need and type of data present.

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31 Discretizes in 10 bins of equal frequency 31

32 Discretizes in 10 bins of equal frequency 32

33 Discretizes in 10 bins of equal frequency 33

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37 Explorer: building classifiers Classifiers in WEKA are machine learning algorithmsfor predicting nominal or numeric quantities Implemented learning algorithms include: Conjunctive rules, decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes nets, 37

38 Explore Conjunctive Rules learner Need a simple dataset with few attributes, let s select the weather dataset

39 Select a Classifier

40 Select training method

41 Right-click to select parameters numantds= number of antecedents, -1= empty rule

42 Select numantds=10

43 Results are shown in the right window (can be scrolled)

44 Can change the right hand side variable

45 Performance data

46 Decision Trees with WEKA

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69 Explorer: clustering data WEKA contains clusterers for finding groups of similar instances in a dataset Implemented schemes are: k-means, EM, Cobweb, X-means, FarthestFirst Clusters can be visualized and compared to true clusters (if given) Evaluation based on loglikelihood if clustering scheme produces a probability distribution 69

70 The K-Means Clustering Method Given k, the k-means algorithm is implemented in four steps: Partition objects into k nonempty subsets Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean point, of the cluster) Assign each object to the cluster with the nearest seed point Go back to Step 2, stop when no more new assignment 70 marzo 11, 2014

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72 right click: visualize cluster assignement

73 Explorer: finding associations WEKA contains an implementation of the Apriori algorithm for learning association rules Works only with discrete data Can 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 73

74 Basic Concepts: Frequent Patterns Tid Items bought 10 Beer, Nuts, Diaper 20 Beer, Coffee, Diaper 30 Beer, Diaper, Eggs 40 Nuts, Eggs, Milk 50 Nuts, Coffee, Diaper, Eggs, Milk Customer buys both Customer buys diaper itemset: A set of one or more items k-itemset X = {x 1,, x k } (absolute) support, or, support count of X: Frequency or occurrence of an itemset X (relative) support, s, is the fraction of transactions that contains X (i.e., the probability that a transaction contains X) An itemset X is frequent if X s support is no less than a minsup threshold 74 Customer buys beer marzo 11, 2014

75 Basic Concepts: Association Rules Tid Customer buys beer Items bought Beer, Nuts, Diaper Beer, Coffee, Diaper Beer, Diaper, Eggs Nuts, Eggs, Milk Nuts, Coffee, Diaper, Eggs, Milk Customer buys both Customer buys diaper Find all the rules X à Y with minimum support and confidence support, s, probability that a transaction contains X Y confidence, c, conditional probability that a transaction having X also contains Y Let minsup = 50%, minconf = 50% Freq. Pat.: Beer:3, Nuts:3, Diaper:4, Eggs:3, {Beer, Diaper}:3 n Association rules: (many more!) n Beer à Diaper (60%, 100%) n Diaper à Beer (60%, 75%) marzo 11, 2014

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82 1. adoption-of-the-budget-resolution=y physician-fee-freeze=n 219 ==> Class=democrat 219 conf:(1) 2. adoption-of-the-budget-resolution=y physician-fee-freeze=n aid-tonicaraguan-contras=y 198 ==> Class=democrat 198 conf:(1) 3. physician-fee-freeze=n aid-to-nicaraguan-contras=y 211 ==> Class=democrat 210 conf:(1) ecc.

83 Explorer: attribute selection 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 83

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92 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 92

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104 References and Resources References: WEKA website: 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: Main_Page Others: Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd ed.