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In Unsupervised Learning, the user doesn’t have to supervise the model. Here, the model is allowed to work on its own to find the information. Here clusters are made [17–21]. A block diagram of unsupervised learning is shown in Figure 1.5.

The figure says that in unsupervised learning the inputs are collected as a set of features that are described as A₁, A₂, A₃, A₄ … … A_k. But, the output features are not available. The input parameters are passed to a learning algorithm module and diverse groups are formed that are called clusters [22–26].

Figure 1.5 Block diagram of unsupervised learning.

Unsupervised Learning has its role in Bioinformatics as concerning the heart disease scenario where inputs can be a lot of symptoms of heart diseases such as High Cholesterol, Chest Pain, and Blood Pressure, etc. These symptoms are passed onto the learning algorithm as input where clusters are made by the model and help the patient for identifying a disease (variables/values of similar types in one cluster) that may occur in near future.