1. Data Mining CSCI 307, Spring 2019 Lecture 7
Output: Trees
WEKA intro

2. Can Use Trees for Numeric Prediction Too
Regression: the process of computing an expression that predicts a numeric quantity
Regression tree: “decision tree” where each leaf predicts a numeric quantity
Predicted value is average value of training instances that reach the leaf
Model tree: “regression tree” with linear regression models at the leaf nodes
Linear patches approximate continuous function
Can Use Trees for Numeric Prediction Too

3. Linear Regression for the CPU Data
PRP =
-56.1
MYCT
MMIN
MMAX
CACH
CHMIN
CHMAX

4. Regression Tree for the CPU Data

5. Model Tree for the CPU Data
LM1 PRP = MMAX CHMIN
LM2 PRP = MMIN – 3.99CHMIN CHMAX
LM3 PRP = MMIN
LM4 PRP = MMAX CACH CHMAX
LM5 PRP = 285 – 1.46MYCT CACH -9.39CHMIN
LM6 PRP = MMIN – 2.94CHMIN CHMAX

6. WEKA Waikato Environment for Knowledge Analysis
On Radius, do this once
(make a WEKA folder, copy all the .arff files, copy the weka jar file) cd
mkdir WEKAfiles
cd WEKAfiles
cp /usr/local/weka-3-8-1/data/* .
cp /usr/local/weka-3-8-1/weka.jar weka.jar
To Run the WEKA application (cd WEKAfiles, if not there already)
java –Xmx1000M -jar weka.jar
To Download onto a Windows or Mac computer, visit:

7. WEKA Introduction
A collection of open source of many data mining and machine learning algorithms, including
pre-processing on data
classification
clustering
association rule extraction
Created by researchers at the University of Waikato in New Zealand.
Java based (also open source).

8. WEKA Main Features ∼ 49 data preprocessing tools
∼ 76 classification/regression algorithms
∼ 8 clustering algorithms
∼15 attribute/subset evaluators + 10 search algorithms for feature selection
∼ 3 algorithms for finding association rules
3 graphical user interfaces
“The Explorer” (exploratory data analysis)
“The Experimenter” (experimental environment)
“The Knowledge Flow” (new process model inspired interface)

9. WEKA

10. WEKA Application Interface
Explorer
preprocessing, attribute selection, learning, visualization
Experimenter
testing and evaluating machine learning algorithms
Knowledge Flow
visual design of the KDD (Knowledge Discovery /from Data/in Databases/with Data mining) process
Simple Command-line
A simple interface for typing commands

11. WEKA Functions and Tools
Preprocessing Filters
Attribute selection
Classification/Regression
Clustering
Association discovery
Visualization

12. WEKA: Pros and Cons Pros Cons Open source, Free Extensible
Can be integrated into other java packages
GUIs (Graphic User Interfaces)
Relatively easy to use
Features
Run individual experiment, or
Build KDD phases
Cons
Lack of proper and adequate documentations
Systems are updated constantly (Kitchen Sink Syndrome)