Md. Saif Kabir Asif has completed

Cluster Analysis in R

4 hours
3,800 XP

Learn How to Perform Cluster Analysis

Cluster analysis is a powerful toolkit in the data science workbench. It is used to find groups of observations (clusters) that share similar characteristics. These similarities can inform all kinds of business decisions; for example, in marketing, it is used to identify distinct groups of customers for which advertisements can be tailored.

Explore Hierarchical and K-Means Clustering Techniques

In this course, you will learn about two commonly used clustering methods - hierarchical clustering and k-means clustering. You won't just learn how to use these methods, you'll build a strong intuition for how they work and how to interpret their results. You'll develop this intuition by exploring three different datasets: soccer player positions, wholesale customer spending data, and longitudinal occupational wage data.

Hone Your Skills with a Hands-On Case Study

You’ll finish the course by applying your new skills to a case study based around average salaries and how they have changed over time. This will combine hierarchical clustering techniques such as occupation trees, preparing for exploration, and plotting occupational clusters, with k-means techniques including elbow analysis and average silhouette widths.

DataCamp courses are comprised of a mixture of videos, articles, and practice exercises so that you have the chance to test and cement your new-found skills so that you feel confident applying them outside a course setting.

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1. 1

Calculating Distance Between Observations

Free

Cluster analysis seeks to find groups of observations that are similar to one another, but the identified groups are different from each other. This similarity/difference is captured by the metric called distance. In this chapter, you will learn how to calculate the distance between observations for both continuous and categorical features. You will also develop an intuition for how the scales of your features can affect distance.

Play Chapter Now
What is cluster analysis?
50 xp
When to cluster?
50 xp
Distance between two observations
50 xp
Calculate & plot the distance between two players
100 xp
Using the dist() function
100 xp
Who are the closest players?
50 xp
The importance of scale
50 xp
Effects of scale
100 xp
When to scale data?
50 xp
Measuring distance for categorical data
50 xp
Calculating distance between categorical variables
100 xp
The closest observation to a pair
50 xp
2. 2

Hierarchical Clustering

This chapter will help you answer the last question from chapter 1—how do you find groups of similar observations (clusters) in your data using the distances that you have calculated? You will learn about the fundamental principles of hierarchical clustering - the linkage criteria and the dendrogram plot - and how both are used to build clusters. You will also explore data from a wholesale distributor in order to perform market segmentation of clients using their spending habits.

3. 3

K-means Clustering

In this chapter, you will build an understanding of the principles behind the k-means algorithm, learn how to select the right k when it isn't previously known, and revisit the wholesale data from a different perspective.

4. 4

Case Study: National Occupational Mean Wage

In this chapter, you will apply the skills you have learned to explore how the average salary amongst professions have changed over time.

In the following tracks

Machine Learning Scientist

Collaborators

Prerequisites

Intermediate R
Dmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center

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