JGS594 Lecture 16

Software Engineering for Machine Learning
Clustering III
(202203)

Transcript

1. jgs SER 594 Software Engineering for Machine Learning Lecture 16: Clustering III Dr. Javier Gonzalez-Sanchez javiergs@asu.edu javiergs.engineering.asu.edu | javiergs.com PERALTA 230U Office Hours: By appointment
2. jgs Previously … Unsupervised Learning
3. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 3 jgs Machine Learning
4. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 4 jgs Algorithms § K-Means - distance between points. Minimize square-error criterion. § DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - distance between nearest points. § Simple EM (Expectation Maximization) is finding likelihood of an observation belonging to a cluster(probability). Maximize log- likelihood criterion
5. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 5 jgs Clustering Algorithms
6. jgs Test Yourselves
7. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 7 jgs DataSet
8. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 8 jgs K-means| K=2
9. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 9 jgs K-means| K=3
10. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 10 jgs K-means| K=4
11. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 11 jgs K-means| K=5
12. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 12 jgs DBSCAN
13. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 13 jgs EM
14. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 14 jgs DataSet
15. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 15 jgs DataSet
16. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 16 jgs K-means | 2 Clusters
17. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 17 jgs K-means | 2 Clusters
18. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 18 jgs EM | 3 Clusters
19. jgs One More Thing Connecting the Dots
20. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 20 jgs One More Thing
21. jgs Assignment
22. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 22 jgs Source Code | Kmeans
23. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 23 jgs Source Code | General
24. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 24 jgs Assignment | Part 1 § https://storm.cis.fordham.edu/~gweiss/data-mining/weka-data/iris.arff § The data was used to learn the description of an acceptable and unacceptable contract. § Number of Instances: 150 § @attribute 'class' {Iris-setosa,Iris-versicolor,Iris-virginica} § K-means (3) § DBSCAN § EM § Evaluation: Likelihood Values § Confusion Matrix, Accuracy
25. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 25 jgs Assignment | Part 2 § https://storm.cis.fordham.edu/~gweiss/data-mining/weka-data/labor.arff § Iris flowers classification § Number of Instances: 57 § @attribute 'class' {'bad’, ’good’} § K-means (2) § DBSCAN § EM § Evaluation: Likelihood Values § Confusion Matrix, Accuracy
26. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 26 jgs Assignment | Part 3 § Students Grades Dataset (From Previous Quiz) § Students' grades § Number of Instances: ~150 § @attribute 'class’ unknown § K-means (?) § DBSCAN § EM § Evaluation: Likelihood Values § Confusion Matrix, Accuracy
27. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 27 jgs Notes § Do not forget to separate Training and Testing datasets § Use your programming skills to calculate Confusion Matrix and Accuracy § As usual submit a paper including: A) Source Code B) Results B) Explain your findings and Conclusions § Academic Integrity 👀
28. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 28 jgs Questions
29. jgs SER 594 Software Engineering for Machine Learning Javier Gonzalez-Sanchez, Ph.D. javiergs@asu.edu Spring 2022 Copyright. These slides can only be used as study material for the class CSE205 at Arizona State University. They cannot be distributed or used for another purpose.