WEKA A Data Mining Tool
By Susan L. Miertschin
1 Data Mining
Task Types Numerous Algorithms Classification C4.5 Decision Tree Clustering K-Means Clustering Discovering Association Rules Discovering Sequential Patterns – Sequence Analysis RiRegression Detecting Deviations from Normal
2 http://www. cs. waikato. ac. nz/ml/weka/
WEKA can be freelyyyg downloaded by visiting the Web site
3 WEKA – Data Mining Software Developed by the Machine Learning Group, University of Waikato , New Zealand Vision: Build state-of-the-art software for developing machine learning (ML) techniques and apply them to real- world data-mining problems DeveloppJed in Java
4 WELA’s Collection of Machine Learning Algorith ms Algorithms for data mining tasks WEKA is open source software issued under the GNU General Public License TlTools for: Data pre-processing Classification Regression Clustering Association rules Visualization
5 After Installing - Start WEKA
6 WEKA Main Interface
7 WEKA Sample Files C:\Program Files\weka\data WEKA formatted files (.arff) Open the contact-lenses file
8 Example – Contact Lens Data
How many data instances are in the file? How many attributes? Numerical attributes? Categorical attributes?
9 Example – Contact Lens Data
Can you think of problems that might be solved with this data?
10 Example – Contact Lens Data If supervised learning were to be done, which would be the output attribute, do you think?
11 Example – Contact Lens Data
12 Example – Contact Lens Data
13 Example – Contact Lens Data
14 ElExample – Classif y - CttContact Lens DDtata
15 ElExample – Classif y - CttContact Lens DDtata
16 ElExample – Classif y - CttContact Lens DDtata
Select the rule generator named PART from the list that shows up after you select Choose
17 ElExample – Classif y - CttContact Lens DDtata
18 ElExample – Classif y - CttContact Lens DDtata
19 10-Fold Cross-Validation Data is partitioned into 10 equally (or nearly equally) sized segments or folds 10 iterations of training and validation are completed In each iteration a ddffifferent fldfold of the data is hhldeld out for validation, with the remaining 9 folds used for learning
20 http://www.public.asu.edu/~ltang9/papers/ency-cross-validation.pdf ElExample – Classif y - CttContact Lens DDtata
21 ElExample – Classif y - CttContact Lens DDtata
IF tear-prod- rate = reduced THEN contact- lenses = none
IF astigma tism = no THEN con tact-leesesnses = soft
22 ElExample – Classif y - CttContact Lens DDtata
Coverage = 12
23 ElExample – Classif y - CttContact Lens DDtata
IF tear-prod- rate = reduced THEN contact- lenses = none
IF astigma tism = no THEN con tact-leesesnses = soft
24 ElExample – Classif y - CttContact Lens DDtata Coverage = 6 Misclassification = 1 Accuracy = 5/6 = 83.3%
25 ElExample – Classif y - CttContact Lens DDtata
26 WEKA A Data Mining Tool
By Susan L. Miertschin
27