Using Weka to experiment learning algorithms. Association rules and clustering
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Weka is a freeware tool written in Java which has a lot of data mining and machine learning algorithms implemented.
You can use it to understand the algorithms, and more interesting for understanding the results you obtained by using the
algorithms. The online lecture from Ian Witten [W1] can help you understand it.

1) Install Weka from the binary from the lab9's folder, or from the Internet
2) Look at the example from the video files from More Data Mining with WEKA [W2]:
a) 3.1: Decision trees and rules   https://www.youtube.com/watch?v=N_V2BmjeYLw
b) 3.2 Generating Decision Rules   https://www.youtube.com/watch?v=ckPh9jYaRWM 
J48 is compared with PART and JRipe, explained in the video
c) 3.3 Asociation rules,   https://www.youtube.com/watch?v=Z4VZsF96QfU
d) 3.4: Learning association rules    https://www.youtube.com/watch?v=N_V2BmjeYLw
Apriori algorithm, ...
e) 3.5 Clustering     https://www.youtube.com/watch?v=HCA0Z9kL7Hg
K-means, X-Means, EM, CobWeb, explained in the video

For Learning asociation rules, you can see the notions of Support, Confidence and Lift explained 
here: https://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html






Bibliography:
[W2] Ian Witten - More Data Mining with Weka, online, last visited, nov 2018, 
https://www.cs.waikato.ac.nz/ml/weka/mooc/moredataminingwithweka/