rjaat/cluster-analysis

Statistical approach to Cluster Analysis

Proximity Measures & Cluster Analysis

Statistical approach to Cluster Analysis in Data Mining using R

Clustering:

• Clustering is a technique in data mining in which, group of different data objects is classified as similar objects.
• One group means a cluster of data.
• Data sets are divided into different groups in the cluster analysis, which is based on the similarity of the data.
• After the classification of data into various groups, a label is assigned to the group. It helps in adapting to the changes by doing the classification.
• So, The process of dividing and storing data in these groups is known as cluster analysis.

Cluster Analysis, Why?

• As a data mining function, cluster analysis serves as a tool to gain insight into the distribution of data to observe characteristics of each cluster.
• Makes Content Analysis Easy.
• Market research, pattern recognition, data analysis, and image processing.
• Identification of areas of similar land use in an earth observation database.
• Classifying documents on the web for information discovery.
• Outlier detection, such as detection of credit card fraud.

Libraries/Packages Used:

• R :
  • tidyverse
  • ggplot2
  • cluster
  • factoextra
  • dbscan

Tasks Performed:

• Data Visualization
• Data Cleaning
• Proximity Measures
• Clustering

References :

• Textbook:

  • An R Companion for Introduction to Data Mining

• Datsets Used :

  • Shapes-Datasets

Note:

For More, Detailed Report Is Attached ['ClusterAnalysis_Report.pdf'].