# Clustering in Machine Learning - Hard Clustering

Hello Readers!! Hope you understood classification and regression algorithms. Now we will proceed to clustering techniques. In simple English, cluster is group of objects of same or different classes. Now the thing is, we have a large number of data values in the available dataset. What we want is to re-group those data values such that similar data values lie in same cluster while distinct data values lie in different clusters.

Clustering is broadly classified in two categories:

**Hard Clustering:**

**Soft Clustering:**

When each data value can belong to more than one cluster.

#### k-Means

- Used for fast clustering of large datasets.
- Used when number of clusters is known.

#### k-Medoids

- Used for fast clustering of large datasets.
- Used when number of clusters is known.
- Used for scaling large datasets.

#### Hierarchical Clustering

- Used when number of clusters is unknown.
- Used to visualize the selection of data point.

#### Self-Organizing Map (SOM)

- Used for 2D representation of the data.
- Used to preserve the topology of the data.

**Reference:** Machine Learning with MATLAB (eBook)

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