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Cluster Analysis Unsupervised Machine Learning Course Bundle


  • Basic knowledge of statistics is required. Some familiarity with data analysis will be considered as an added advantage though it is not a necessity.


Cluster Analysis is a statistical tool which is used to classify objects into groups called clusters, where the objects belonging to one cluster are more similar to the other objects in that same cluster and the objects of other clusters are completely different. In simple words cluster analysis divides data into clusters that are meaningful and useful. Clustering is used mainly for two purposes – clustering for understanding and clustering for utility.

Application of cluster analysis

  • Cluster analysis is used in many fields like machine learning, market research, pattern recognition, data analysis, information retrieval, image processing and data compression.
  • Cluster analysis can help the marketers to find out distinct groups of their customer base.
  • Cluster analysis is used in the field of biology to find out plant and animal taxonomies and categorize genes with similar characteristics
  • Cluster analysis is used in an earth observation database to group the houses in a city according to the house type, value and location.
  • Clustering can also be used to segment the documents on the web based on a specific criteria
  • In data mining, cluster analysis is used to gain in-depth understanding about the characteristics of data in each cluster.

Clustering Methods

Clustering methods can be divided into the following categories

  • Partitioning method
  • Hierarchical Method
  • Density based method
  • Grid Based Method
  • Model Based Method
  • Constraint Based Method

Advantages of Cluster Analysis

Given below are the advantages of cluster analysis

  • Cluster analysis gives a quick overview of data
  • It can be used if there are many groups in data
  • Cluster analysis can be used when there are unusual similarity measures to be done
  • Cluster analysis can be added on ordination plots and it is good for the nearest neighbours

Approaches to cluster analysis

There are a number of different approaches used to carry out cluster analysis which are divided into two

  • Hierarchical Method – Agglomerative Methods and Divisive Methods
  • Non Hierarchical Method also known as K-means Clustering methods

Cluster Analysis Course Objectives

At the end of this course you will be able to know

  • How to use cluster analysis in data mining
  • About the various types of clusters
  • About the Marketing applications of cluster analysis
  • Implications of wide variety of clustering techniques
  • Use clustering in statistical analysis

Who this course is for:

  • Students, Research professionals, Data Analysts, Data Miners And anyone who is interested in learning about cluster analysis

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