In contrast to supervised learning that usually makes use of labelled data, unsupervised learning looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. There are wide open implementation of unsupervised learning with unstructured big data which includes but not limited to areas such as:
- Audience segmentation
- Pattern recognition (grouping images, transcribing audio)
- Social Media Influencer identification
- Customer persona investigation
- Anomaly detection (for example, to detect bot activity)
- deep learning, etc.
The two techniques in Unsupervised Learning techniques are:
- Clustering
- Association
1. Clustering
