Getting started with Weka. Yishuang Geng, Kexin Shi, Pei Zhang, Angel Trifonov, Jiefeng He, Xiaolu Xiong
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1 Getting started with Weka Yishuang Geng, Kexin Shi, Pei Zhang, Angel Trifonov, Jiefeng He, Xiaolu Xiong
2 Lesson Introduction
3 Purpose of this course Take the mystery out of data mining. How to use the Weka workbench for data mining. Explain the basic principles of several popular algorithms
4 Data mining with Weka What s data mining? We are overwhelmed with data Data mining is about going from the raw data to information. What could data mining do? You re at the supermarket checkout and you re happy with your bargains and the supermarket is happy you ve bought some more stuff You want a child, but you and your partner can t have one.
5 What is Weka? 1. A bird found only in New Zealand 2. Waikato Environment for Knowledge Analysis Weka includes: 100+ algorithms for classification 75 for data preprocessing 25 to assist with feature selection 20 for clustering, finding association rules, etc
6 Textbook Data Mining: Practical machine learning tools and techniques, by Ian H. Witten, Eibe Frank and Mark A. Hall. Morgan Kaufmann, 2011
7 Learning outcome of the course Load data into Weka and look at it Use filters to preprocess it Explore it using interactive visualization Apply classification algorithms Interpret the output Understand evaluation methods and their implications Understand various representations for models Explain how popular machine learning algorithms work Be aware of common pitfalls with data mining Use Weka on your own data and understand what you are doing!
8
9 A simple application You want to monitor the firefighters status but you cannot get into the burning houses to watch them.
10 A simple application Motion Detection Using RF Signals for the First Responder in Emergency Operations Firefighters Sensor to monitor their physiological information, which personal area communication capability to a centroid node. Centroid node has local area communication capability to link the terminals out of burning house. If we want to monitor their motion, what should we do?
11 Existing approaches Pros High detection rate. Low computational cost. Cons Add extra load to firefighter. Limited sensor location, usually on shoes. Lack of capability on detecting multiple motions,mainly used for fall detection.
12 Raw data
13 Data mining
14 Information from the raw data
15 Summary Why taking that course Materials Weka Textbook Course schedule Lectures Activities Assessments Learning outcome A simple application
16 Lesson Exploring the Explorer
17 Setting up Weka Download latest (Weka ) from waikato.ac.nz/ml/weka/downloading.html Self-extracting executable Java VM included (if needed) Create shortcut to Data folder in your Computer s My Documents Use the Weka shortcut from the program folder
18 Weka Interface Weka interfaces Explorer Experimenter GUI Command-line Explorer will be used the most
19 Explorer Interface Explorer Panels Preprocess Opening datasets File Filter Supervised Unsupervised
20 Filters Difference An additional two kinds of filtering Instances Attributes
21 More Preprocess Information Relation Attributes Instances Selected Attribute Name Type Other Info Attributes Editing Removing Class Visualization Status and log
22 Lesson Exploring datasets
23 Classification
24
25 Nominal vs. Numerical
26 ARFF file format
27
28
29
30 Lesson Building a classifier Classifying the glass dataset Interpreting J48 output J48 configuration panel... option: pruned vs unpruned trees... option: avoid small leaves Jiefeng
31
32 Click Here
33 What the percentage classified instances? Use theis3 confusion matrix ofcorrectly to determine how many headlamps instances were misclassified as build wind float?
34
35
36 Turning pruning off results in larger trees, and often yields worse results because the classifier may "overfit" the data. However, in some cases the unpruned tree performs better
37
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40
41
42
43 1.4 Summary Building a classifier Classifying the glass dataset Interpreting J48 output J48 configuration panel... option: pruned vs unpruned trees... option: avoid small leaves
44 Lesson Using a filter
45 Use a filter to remove an attribute Open weather.nominal.arff
46 Check the filters
47
48 Set attributeindices to 3 and click OK
49 Apply the filter
50
51
52
53
54
55
56
57
58
59
60
61 Lesson Visualizing your data
62 Raw data visualization
63 Sepalwidth vs. petalwidth
64
65 Zoom in
66 Zoom in
67 Error visualization
68 Error visualization
69 Thank you! Questions?
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