JGS594 Lecture 16

Software Engineering for Machine Learning
Clustering III
(202203)

Transcript

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   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
   

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   Previously …
   Unsupervised Learning
   

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3. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 3
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   Machine Learning
   

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4. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 4
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   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
   

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5. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 5
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   Clustering Algorithms
   

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   Test Yourselves
   

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7. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 7
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   DataSet
   

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8. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 8
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   K-means| K=2
   

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   K-means| K=3
   

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    K-means| K=4
    

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    K-means| K=5
    

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12. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 12
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    DBSCAN
    

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13. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 13
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    EM
    

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14. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 14
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    DataSet
    

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    DataSet
    

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    K-means | 2 Clusters
    

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    K-means | 2 Clusters
    

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18. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 18
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    EM | 3 Clusters
    

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    One More Thing
    Connecting the Dots
    

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20. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 20
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    One More Thing
    

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    Assignment
    

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    Source Code | Kmeans
    

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    Source Code | General
    

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24. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 24
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    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
    

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25. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 25
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    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
    

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26. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 26
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    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
    

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27. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 27
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    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 👀
    

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28. Javier Gonzalez-Sanchez | SER 594 | Spring 2022 | 28
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    Questions
    

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    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.
    

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