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The method of making groups of similar characteristics is known as cluster analysis. In cluster analysis, the objects in one group are more similar as compared to the other groups. It is very common in statistics and used in fields like image analysis, computer graphics, machine learning.
A few algorithms of cluster analysis are mentioned here.
Connectivity based clustering- The main idea of connectivity clustering is that the objects are similar to the objects nearer to them rather than far away. The clusters or groups are formed based on the distance in connectivity clustering. The two strategies in connectivity clustering are agglomerative and divisive. In connectivity-based clustering, the hierarchy of clusters is formed from the objects at distance merged.
Centroid based or K- means clustering- It is represented by a central vector. The vector may or may not be a part of the data set. It is also known as k-means clustering. In centroid based clustering the cluster or k are predefined. Each observation belongs to the nearest mean. It has other applications such as feature learning and vector quantization. The k cluster is assigned to the nearest cluster centre. These are assigned in a manner that the squared distance is minimum from the cluster.
Distribution based clustering- Distribution models are the most closely related model to statistics. The clusters are formed with the objects of the same distribution. Overfitting is one of the big hurdles that lie in distribution based clustering. The Gaussian mixture model is one of the prominent models used in distribution based clustering.
Density-based clustering- Rather than the remainder of the data set, the clusters in density models are defined based on the area of high density. Density-based spatial clustering of applications with noise (DBSCAN) is the most popular model in density clustering. This model also makes clusters of the objects based on the distance.
Grid-based clustering- For the multi-dimensional data set, grid-based clustering is used. STING and CLIQUE are the two methods of grid-based clustering.
Steps in grid-based clustering
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