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JGS594 Lecture 16

JGS594 Lecture 16

Software Engineering for Machine Learning
Clustering III
(202203)

B546a9b97d993392e4b22b74b99b91fe?s=128

Javier Gonzalez
PRO

March 31, 2022
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  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.