Clustering
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This document discusses unsupervised learning and clustering. It provides examples of applications of unsupervised learning like social network analysis, market segmentation, and fraud detection. It then focuses on clustering, explaining that k-means clustering is an algorithm that groups unlabeled data points into k number of clusters by minimizing distances between points and cluster centroids. The k-means algorithm works by initially selecting k centroids and then iteratively recomputing centroids and reassigning points to clusters until changes are below a threshold.
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Speaker: Marcel Spitzer
Blog-Artikel: https://www.inovex.de/blog/machine-learning-interpretability/
Mehr Tech-Vorträge: inovex.de/vortraege
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Clustering
- 2. Machine Learning ● Supervised Learning ● Unsupervised Learning
- 3. Machine Learning ● No Labels or right answers given ● Pattern or Structure needs to be identified ● Not told what to do with data. Unsupervised Learning
- 4. Unsupervised Learning ➢ Social Network Analysis ➢ Market Segmentation ○ Grouping Customers ➢ News ➢ Images that has Human Face.Clustering
- 5. Unsupervised Learning ➢ Audio refinement - Cocktail Party Algorithm ➢ Fraud Detection ➢ Default detection Non-Clustering
- 6. Unsupervised Learning - Clustering
- 7. Unsupervised Learning - K Means k-means
- 8. K-means Algorithm 1. Select K points as the initial Centroids 2. REPEAT 3. Form K clusters by assigning all points to the closest Centroid 4. Recompute the Centroid for each cluster 5. UNTIL “The Centroids don’t change or all changes are below predefined threshold”
- 10. K - Means Algorithm Mini ● Minimize distortion by sum of the squared distortion between each observation and its closest centroid. ● Stop when change in distortion is lower than a pre established threshold. Distortion Function