Clustering
What is clustering?
Clustering is the task of dividing the population or data points into several groups such that data points in the same groups are more similar to the data points in the same group and dissimilar to the data points in the other groups. It is a collection of objects based on similarities and dissimilarities between them. It belongs to a category of unsupervised learning methods.
Why clustering?
Clustering is important as it determines the intrinsic grouping among the unlabelled data present. The criteria for clustering depend on the user that satisfies their requirements. It has a myriad of uses in a variety of industries. Some of the applications are:
- Market segmentation
- Anomaly detection
- Search result grouping
- Social network analysis
What are the types of clustering algorithms?
The types of clustering algorithms are:
- Density-based methods- This method considers the clusters from the dense region having some similarities and differences from the lower region of the space. They have good accuracy and the ability to merge two clusters.
- Hierarchical-based methods- This method forms a tree-type structure based on the hierarchy. New clusters are formed based on the previous one.
- Partitioning methods- This method partition the objects into k clusters, and each partition forms one cluster.
- Grid-based methods- The data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operations done on these grids are fast and independent of the number of data objects.
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