Unsupervised Learning
Unsupervised Learning: the program is given a bunch of unlabelled data and must discover patterns and relationships in them.
In unsupervised learning:
- we are provided with a data set
- we need to find an understanding of interesting relationships within the data
- we identify groups / clusters of interesting information within the data
- can be used to identify where new points ‘fit’ within the data set
We use two common techniques: clustering and association rules.
Link to original
Clustering
Clusters
Clusters are formed by similar patterns. One common similarity metric is distance:
- Euclidean distance: shortest distance between two points
- Manhattan distance: distance between two points measured along axis at right angles
Clustering algorithms are NP-hard problems, so the number of possible ways to cluster items increases exponentially. So we look for efficient ways to find good clusters using a combination of metric and heuristic search.
Link to original
We look at: