Clustering Unsupervised learning introduction Machine Learning Supervised learning
- Slides: 29
Clustering Unsupervised learning introduction Machine Learning
Supervised learning Training set: Andrew Ng
Unsupervised learning Training set: Andrew Ng
Applications of clustering Market segmentation Social network analysis Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison) Organize computing clusters Astronomical data analysis Andrew Ng
Clustering Machine Learning K-means algorithm
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K-means algorithm Input: (number of clusters) - Training set (drop convention) Andrew Ng
K-means algorithm Randomly initialize cluster centroids Repeat { for = 1 to : = index (from 1 to ) of cluster centroid closest to for = 1 to : = average (mean) of points assigned to cluster } Andrew Ng
K-means for non-separated clusters Weight T-shirt sizing Height Andrew Ng
Clustering Optimization objective Machine Learning
K-means optimization objective = index of cluster (1, 2, …, ) to which example assigned = cluster centroid ( ) = cluster centroid of cluster to which example assigned Optimization objective: is currently has been Andrew Ng
K-means algorithm Randomly initialize cluster centroids Repeat { for = 1 to : = index (from 1 to ) of cluster centroid closest to for = 1 to : = average (mean) of points assigned to cluster } Andrew Ng
Clustering Random initialization Machine Learning
K-means algorithm Randomly initialize cluster centroids Repeat { for = 1 to : = index (from 1 to ) of cluster centroid closest to for = 1 to : = average (mean) of points assigned to cluster } Andrew Ng
Random initialization Should have Randomly pick examples. Set examples. training equal to these Andrew Ng
Local optima Andrew Ng
Random initialization For i = 1 to 100 { Randomly initialize K-means. Run K-means. Get Compute cost function (distortion) . } Pick clustering that gave lowest cost Andrew Ng
Clustering Choosing the number of clusters Machine Learning
What is the right value of K? Andrew Ng
Choosing the value of K Cost function Elbow method: 1 2 3 4 5 6 (no. of clusters) 7 8 1 2 3 4 5 6 7 8 (no. of clusters) Andrew Ng
Choosing the value of K Sometimes, you’re running K-means to get clusters to use for some later/downstream purpose. Evaluate K-means based on a metric for how well it performs for that later purpose. E. g. T-shirt sizing Weight T-shirt sizing Height Andrew Ng
- Supervised learning dan unsupervised learning
- Perbedaan supervised dan unsupervised
- Supervised vs unsupervised learning
- Supervised and unsupervised learning
- Supervised vs unsupervised data mining
- Supervised vs unsupervised data mining
- Unsupervised hierarchical clustering
- Nyt top stories
- Partitional clustering vs hierarchical clustering
- Rumus distance
- Unsupervised learning in data mining
- Transductive learning for unsupervised text style transfer
- Autoencoders
- Ann unsupervised learning
- Workspca
- Unsupervised learning
- Machine learning andrew ng
- Contractive autoencoder
- Alexandru niculescu-mizil
- Supervised learning pipeline
- Partially supervised learning
- Eli gutin
- Introduction to machine learning ethem alpaydin
- Andrew ng introduction to machine learning
- Mike mozer
- Machine learning ethem
- Machine learning lecture slides
- Introduction to machine learning slides
- Introduction to machine learning and data mining
- A friendly introduction to machine learning