Centroid Based Clustering

Centroid Based Clustering

Centroid Based Clustering

Description

In Centroid Based Clustering, a central vector represents each cluster. The objects are assigned to the clusters such that the squared distance between the object and the central vector is minimized.

Why to use

To convert textual data to its numerical form.

When to use

  • When the number of clusters is known.
  • When each cluster size is expected to be of equal size.

When not to use

  • When data is labeled.
  • When the number of clusters is not known.
  • When clusters are not to be of equal size.

Prerequisites

Input data should be of text type and should not contain special characters and numbers.

Input

Textual Data

Output

Data divided into clusters

Statistical Methods used

  • K-means
  • Random Initialization

Limitations

  • The number of clusters needs to be known.
  • Not very robust to outliers.
  • Does not work very well with non-convex shapes.
  • Tries to generate equal-sized clusters.

In Centroid-based clustering, each cluster is represented by a central vector. The central vector may not necessarily be a part of the dataset. A data value is assigned to a cluster depending upon its proximity, such that its squared distance from the central vector is minimized.

The k-means algorithm is the most widely used centroid-based clustering algorithm. In this algorithm, the dataset is divided into k pre-defined, distinct, and non-overlapping clusters. Each data point is assigned to a cluster such that the arithmetic means of all data points within a cluster is always minimum. Minimum variation within a cluster ensures greater homogeneity of data points within that cluster.

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