In Data mining, Clustering is a type of unsupervised learning algorithm i.e. training a model on the unlabeled data. Clustering is a process of grouping similar observations in one cluster and dissimilar observations in another cluster. These clustering algorithms calculate the similarity between the observations using similarity measures such as Euclidean distance, Manhattan distance, Cosine similarity, Jaccard distance, or Minkowski distance.
There are three types of clustering algorithms as follows:
- Partitional clustering
- Density-based clustering
- Hierarchal clustering.
The most commonly used clustering algorithms are K-means, DBSCAN, and Agglomerative clustering.
If you wish to learn more about clustering and implementation, you can enroll in these Data Scientist course by Intellipaat.
Also, watch this video on Data Mining:
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