Data clustering consists of data mining methods for identifying groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial.
Similarity between observations (or individuals) is defined using some inter-observation distance measures including Euclidean and correlation-based distance measures.
There are different types of data clustering techniques, including:
- Partitioning clustering approaches, which subdivide the data into a set of k groups. One of the popular partitioning method is the k-means clustering
- Hierarchical clustering approaches, which identify groups in the data without subdividing it.
This course presents the basics to know for clustering analysis in R. You will learn:
- Data preparation and essential R packages for cluster analysis
- Clustering distance measures essentials
- Quick start R code to perform k-means clustering and hierarchical clustering in R.
Practical Guide to Cluster Analysis in R