1. An Introduction to WEKA
Saeed Iqbal

2. Content
What is WEKA?
Data set in WEKA
The Explorer:
Preprocess data
Classification
Clustering
Association Rules
Attribute Selection
Data Visualization
References and Resources
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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.
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4. Download and Install WEKA
Website:
http://www.cs.waikato.ac.nz/~ml/weka/index.html
Support multiple platforms (written in java):
 Windows, Mac OS X and Linux
Weka Manual:
http://transact.dl.sourceforge.net/sourcefor
ge/weka/WekaManual-3.6.0.pdf
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5. 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
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6. 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 GUI
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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 Files
Weka wants its input data in ARFF format.
A dataset has to start with a declaration of its name:
 @relation name
@attribute attribute_name specification
 If an attribute is nominal, specification contains a list of the possible
attribute values in curly brackets:
 @attribute nominal_attribute {first_value, second_value,
third_value}
 If an attribute is numeric, specification is replaced by the keyword
numeric: (Integer values are treated as real numbers in WEKA.)
 @attribute numeric_attribute numeric
After the attribute declarations, the actual data is introduced by
a tag:
 @data

9. ARFF File
@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

10. 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.

11. Content
What is WEKA?
Data set in WEKA
The Explorer:
Preprocess data
Classification
Clustering
Association Rules
Attribute Selection
Data Visualization
References and Resources
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12. 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, …
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13. WEKA only deals with “flat” files
@relation heart-disease-simplified
@attribute age numeric
@attribute sex { female, male}
@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}
@attribute cholesterol numeric
@attribute exercise_induced_angina { no, yes}
@attribute class { present, not_present}
@data
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
...
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14. WEKA only deals with “flat” files
@relation heart-disease-simplified
@attribute age numeric
@attribute sex { female, male}
@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}
@attribute cholesterol numeric
@attribute exercise_induced_angina { no, yes}
@attribute class { present, not_present}
@data
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
...
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15. load
filter
analyze
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36. Explorer: building “classifiers”
Classifiers in WEKA are models for predicting nominal or
numeric quantities
Implemented learning schemes include:
Decision trees and lists, instance-based classifiers, support
vector machines, multi-layer perceptrons, logistic regression,
Bayes’ nets, …
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37. Decision Tree Induction: Training Dataset
age income student credit_rating buys_computer
<=30 high no fair no
This follows <=30 high no excellent no
31…40 high no fair yes
an example >40 medium no fair yes
of Quinlan’s >40 low yes fair yes
ID3 (Playing >40 low yes excellent no
31…40 low yes excellent yes
Tennis) <=30 medium no fair no
<=30 low yes fair yes
>40 medium yes fair yes
<=30 medium yes excellent yes
31…40 medium no excellent yes
31…40 high yes fair yes
>40 medium no excellent no
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38. Output: A Decision Tree for “buys_computer”
age?
<=30 overcast
31..40 >40
student? yes credit rating?
no yes excellent fair
no yes no yes
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39. Algorithm for Decision Tree Induction
 Basic algorithm (a greedy algorithm)
Tree is constructed in a top-down recursive divide-and-conquer
manner
At start, all the training examples are at the root
Attributes are categorical (if continuous-valued, they are discretized
in advance)
Examples are partitioned recursively based on selected attributes
Test attributes are selected on the basis of a heuristic or statistical
measure (e.g., information gain)
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40. classifiers
DEMO

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63. 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
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64. Clustering
DEMO

65. 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
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66. 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
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67. Basic Concepts: Frequent Patterns
Tid Items bought  itemset: A set of one or more items
10 Beer, Nuts, Diaper  k-itemset X = {x1, …, xk}
20 Beer, Coffee, Diaper
 (absolute) support, or, support count of X:
30 Beer, Diaper, Eggs
Frequency or occurrence of an itemset
40 Nuts, Eggs, Milk X
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 (relative) support, s, is the fraction of
Customer Customer transactions that contains X (i.e., the
buys both buys diaper probability that a transaction contains X)
 An itemset X is frequent if X’s support is
no less than a minsup threshold
Customer
buys beer
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68. Basic Concepts: Association Rules
Tid Items bought  Find all the rules X  Y with minimum
10 Beer, Nuts, Diaper
support and confidence
20 Beer, Coffee, Diaper
30 Beer, Diaper, Eggs  support, s, probability that a
40 Nuts, Eggs, Milk transaction contains X ∪ Y
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 confidence, c, conditional probability
Customer
Customer that a transaction having X also
buys both
buys contains Y
diaper
Let minsup = 50%, minconf = 50%
Freq. Pat.: Beer:3, Nuts:3, Diaper:4, Eggs:3, {Beer,
Diaper}:3
Customer
buys beer  Association rules: (many more!)
 Beer  Diaper (60%, 100%)
 Diaper  Beer (60%, 75%)
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69. Association
DEMO

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75. 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
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76. Explorer: attribute selection
DEMO

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85. 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
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86. data visualization
DEMO

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97. References and Resources
 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.