What are different clustering methods

Question

What are the different clustering methods used in machine learning?

Answer 1

Different clustering methods include:

1. Density-Based Methods: These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and the ability to merge two clusters.

2. Hierarchical Based Methods: The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category
Agglomerative (bottom-up approach)
Divisive (top-down approach)

3. Partitioning Methods: These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means

4. Grid-based Methods: In this method, the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid)