CLARA is a clustering technique that extends the k-medoids (PAM) methods to deal with data containing a large number of objects in order to reduce computing time and RAM storage problem. In this article, you will learn: 1) the basic steps of CLARA algorithm; 2) Examples of computing CLARA in R software using practical examples. The k-medoids (or PAM) algorithm is a non-parametric alternative of k-means clustering for partitioning a dataset. This article describes the PAM algorithm and shows how to compute PAM in R software. K-means clustering is one of the most commonly used unsupervised machine learning algorithm for partitioning a given data set into a set of k groups. In this tutorial, you will learn: 1) the basic steps of k-means algorithm; 2) How to compute k-means in R software using practical examples; and 3) Advantages and disavantages of k-means clustering This chapter describes a cluster analysis example using R software. We provide a quick start R code to compute and visualize K-means and hierarchical clustering. In this article, we describe the common distance measures used to compute distance matrix for cluster analysis. We also provide R codes for computing and visualizing distances. This chapter introduces how to prepare your data for cluster analysis and describes the essential R package for cluster analysis.
Compute Summary Statistics in R
This tutorial introduces how to easily compute statistcal summaries in R using the dplyr package. You will learn, how to compute summary statistics for ungrouped data, as well as, for data that are grouped by one or multiple variables. This tutorial describes how to compute and add new variables to a data frame in R. You will learn how to rename a data frame columns in R. This tutorial describes how to reorder rows, in your data table, by the value of one or more variables. You will learn how to easily sort a data frame rows in ascending and descending orders.