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When a machine learns from unlabeled data or it discovers the input pattern itself, it is known as unsupervised learning. It divides the learning data into diverse clusters. Therefore, this learning is known as clustering algorithm. In this learning, the training data will not be labeled and inferences functions create its own inferences by exploring the unlabeled dataset in order to find suitable patterns [6]. Figure 1.3 shows the complete process of unsupervised learning.

Name of common unsupervised algorithms:

 • Anomaly detection

 • K-means clustering

 • Neural networks

 • Hierarchal clustering

 • Independent component analysis

 • Principle component analysis