Unsupervised learning

• Data does not have labels (y values)
• Clustering is a way to find structures with in data sets.
• Applications of clustering
  • Market segmentation
  • Astronomy
  • Computer clusters
  • Social network analysis.

K-means algorithm

Fine tuning k-means

• Use randomly chosen inputs from input data set as initial centroids.
• Run k means multiple times with different randomly chosen centroids to reduce chance of getting stuck in local optima.
• Use elbow method to find optimum number of clusters. Although elbow method is quite ineffective for most real world problems.
• Deciding number of clusters requires human insight into the problem domain.

Dimensionality reduction

• Useful to reduce number of features to 2-3 to aid in visualizing data. May not necessarily be used to write machine learning algorithms.
• Compression - to save disk space, specially with computer vision.
• Speed up learning algorithm by reducing number of features without losing variance.

Don’t use PCA to solve overfitting.

Principal Component analysis

How to choose number of Principal components