jgs CSC 570 Current Topics in Computer Science Applied Affective Computing Lecture 06: Clustering Dr. Javier Gonzalez-Sanchez [email protected] www.javiergs.com Building 14 -227 Office Hours: By appointment
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 5 § 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 Algorithms
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 16 A summary of prediction results on a classification problem Definition 6 3 3 2 YES 0 NO 1 YES 0 NO 1
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 18 § overall measure of how much the model is correctly predicting on the entire set of data § Addition of the elements in the main diagonal divide by the sum of all the entries of the confusion matrix at the denominator. Definition
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 22 Precision and Recall / / How much can we trust the model when predicting a Positive Precision = TP / TP + FP Precision = 6 / 6 + 3 Precision = 6 / 9 = 0.66 / / Measure the ability of the model to find all Positive units Recall = TP / TP + FN Recall = 6 / 6 + 3 Recall = 6 / 9 = 0.66 TP FP FN TN YES 0 NO 1 YES 0 NO 1
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 25 § The harmonic mean of precision and recall. § Mixture of: How much we can trust the model when predict a Positive (Precision), and The ability of the model to find all Positive units (Recall) Definition
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 26 Harmonic mean a = 7 b = 3 / / if same units (big) Arithmetic mean = 7+3 / 2 = 5 / / if diverse units (small) Geometric mean = sqrt (7*3) = 4.58 / / ratios of diverse units (smaller) Harmonic mean = pow (sqrt (7*3)) / (7+3 / 2) = 21 / 5 = 4.2
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 32 Definition positive negative TP FP FN TN positive negative FN X FP X TN / / How much we can trust the model when predict a Positive Precision = TP / TP + FP / / Measure the ability of the model to find all Positive units Recall = TP / TP + FN
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 36 no no yes yes yes no yes no yes yes yes yes yes no K-means, DBSCAN, EM K-means DBSCAN EM 8/14 8/14 10/14
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 37 Homework • Open or Closed Eyes VS. Brain • 5 diverse stimulation scenarios VS. Brain • 5 diverse stimulation scenarios VS. Affect Follow 2 approaches: a) Clustering as described today (EM, K-means, Density) b) Explore another solution to the best of your knowledge (Machine Learning, Data mining, Statistics) Due: Monday (April 24)
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 42 Thoughts? Clustering was easy, What about something more precise for our data (such as a RandomForest) ?
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 44 § 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 Just To Practice
jgs Javier Gonzalez-Sanchez | CSC 309 | Winter 2023 | 47 Office Hours Tuesday and Thursday 3 - 5 pm But an appointment required Sent me an email – [email protected]
jgs CSC 570 Applied Affective Computing Javier Gonzalez-Sanchez, Ph.D. [email protected] Spring 2023 Copyright. These slides can only be used as study material for the class CSC308 at Cal Poly. They cannot be distributed or used for another purpose.
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