Clustering is the machine learning task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Given a set of data points, we can use a clustering algorithm to group each data point into a specific group. In theory, data points that are clustered in the same group should have similar properties or features, while data points in different groups should have highly distinct properties or features. Clustering is a common technique for statistical data analysis, and is used in many fields.

There are different types of clustering algorithm. The following are the most common clustering algorithms:

•  K-means clustering algorithm
• Mean-shift clustering
• Agglomerative-hierarchical clustering
• Density-Based Spatial Clustering

We use clustering for IMDb because similar datasets are very close to each other. For example, the same crew might shoot movies that have similar average ratings. So we want to use clustering to determine what can lead to different clusters in IMDb datasets. In other words, we want to see what is the most important factor affecting clustering in the IMDb dataset.