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Data Clustering Assignment Help

Updated: Apr 27, 2022

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What is clustering ?

Clustering is the method of grouping a set of objects in such a way that objects in the same group are called clusters and are more similar to each other than to those in other groups (clusters). It is a common technique for statistical data analysis used in many domains, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and machine learning

Types of clustering

There are different types of clustering algorithms

  • Centroid based clustering

  • Connectivity based clustering

  • Density based clustering

  • Grid based clustering

  • Distribution based clustering

Centroid based clustering : In this each cluster is represented by a central vector, which is not necessarily a member of the data set. When the number of clusters is fixed to k, k means clustering gives a formal definition as an optimization problem: find the k cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized.

Connectivity based clustering : Connectivity-based clustering, also known as hierarchical clustering, is based on the core idea of objects being more related to nearby objects than to objects farther away. These algorithms connect "objects" to form "clusters" based on their distance. A cluster can be described largely by the maximum distance needed to connect parts of the cluster.

Density based clustering : Most popular density based algorithm is the DBSCAN algorithm. In density-based clustering, clusters are defined as areas of higher density than the remainder of the data set. Objects in sparse areas – that are required to separate clusters – are usually considered to be noise and border points.

Distribution based clustering : The clustering model most closely related to statistics is based on distribution models. Clusters can then easily be defined as objects belonging most likely to the same distribution. A convenient property of this approach is that this closely resembles the way artificial data sets are generated: by sampling random objects from a distribution.

Grid based clustering : The grid-based technique is used for a multi dimensional data set. In this technique, we create a grid structure, and the comparison is performed on grids (also known as cells). The grid-based technique is fast and has low computational complexity.

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