The document outlines an introduction to unsupervised learning using Orange3, a Python data mining library. It describes the differences between supervised and unsupervised learning, detailing clustering algorithms such as k-means and providing resources for tutorials and examples. Additionally, it includes homework assignments for hands-on practice with clustering and association rules using personal datasets.

1. Unsupervised
Learning
Orozco Hsu
2022-04-11 1

2. About me
• Education
• NCU (MIS)、NCCU (CS)
• Work Experience
• Telecom big data Innovation
• AI projects
• Retail marketing technology
• User Group
• TW Spark User Group
• TW Hadoop User Group
• Taiwan Data Engineer Association Director
• Research
• Big Data/ ML/ AIOT/ AI Columnist
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3. Tutorial
Content
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The Orange 3 introduction
Getting started unsupervised learning with
Orange3
Home work
What is the unsupervised learning/ K-Means

4. Orange Data Mining
• Orange 3 introduction
• Orange Data Mining (agnieszka.si)
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5. Orange Data Mining
• How to use Orange 3
• https://www.youtube.com/@OrangeDataMining
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6. Download Orange for Windows
• A Python 3 data mining library with GUI.
• https://orangedatamining.com/screenshots/
• Widget catalogs
• Orange Data Mining - Widget catalog
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7. 7

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9. Orange3 Canvas
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10. Add-on
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Note: Once you upgrade the Orange3 version, all the Add-on you have installed will be empty

11. Output the Orange3 workflow file
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12. Basic Orange3
• Add-on
• Associate (1.2.0)
• Explain (0.6.2)
• Educational (0.6.0)
• Image Analytics (0.10.0)
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13. Basic Orange3
• Use File/Datasets widget and display dataset with Data-Table and
Scatter plot
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14. Supervised learning vs. Unsupervised learning
• Supervised learning: discover patterns in the data that relate data
attributes with a target (class) attribute.
• These patterns are then utilized to predict the values of the target attribute in
future data instances.
• Unsupervised learning: The data have no target attribute.
• We want to explore the data to find some intrinsic structures in them.
• Classic unsupervised learning algorithm
• Clustering algorithms
• Association rules
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15. Clustering (K-means algorithm)
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16. Clustering (K-means algorithm)
• Steps
• Define number K (cluster groups)
• Randomize the centroids of each cluster, calculating the summary
of distances of each data point to the centroids.
• Move the centroids and re-calculating the summary of distances,
until the summary of distances are in the convergency.
• https://www.youtube.com/watch?v=5I3Ei69I40s
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17. Clustering of data
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18. Scatter plot of data
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19. Orange3 Interactive K-Means
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20. Orange3 K-Means
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21. Hierarchical clustering
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22. Image Clustering
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23. Homework
• Use your own dataset to find the best number K cluster and
explain each cluster statistic information
• Use Groceries data.csv for association rules and demo it.
• https://orangedatamining.com/blog/2016/04/25/association-rules-
in-orange/
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