Visualize clusters

1. Visualize clusters

Hi everyone! Now that you are familiar with hierarchical clustering and how the algorithm works, let us take a step in the direction of visualizing clusters.

2. Why visualize clusters?

Why do we need to visualize clusters? One can quickly make sense of the clusters formed by any algorithm by visually analyzing it rather than just looking at cluster centers. It can serve as an additional step for validation of clusters formed. Additionally, you may also spot trends in your data by visually going through it. Let us now look at possible ways of visualizing the clusters that we have formed in our earlier exercise.

3. An introduction to seaborn

Seaborn is a data visualization library in Python that is based on matplotlib. It provides better default plotting themes, which can be easily and intuitively modified. It has functions for quick visualizations in the context of data analytics. In this course on clustering, we use pandas DataFrames to store our data, often adding a separate column for cluster centers. Seaborn provides an argument in its scatterplot method to allow us to use different colors for cluster labels to differentiate the clusters when visualizing them. Let us compare the implementation of the two plotting techniques - matplotlib and seaborn.

4. Visualize clusters with matplotlib

To visualize clusters, we first import the pyplot class in matplotlib. Let us start with a pandas DataFrame which has the columns - x, y and label for its x and y coordinates and cluster labels, A and B. We will use the c argument of the scatter method, to assign a color to each cluster. However, we first need to manually map each cluster to a color. Therefore, we define a dictionary named colors with the cluster labels as keys, and the color associated with the clusters as its values. We then pass a list of colors to c argument using a lambda function, which returns the corresponding value of each cluster label.

5. Visualize clusters with seaborn

The implementation in seaborn is fairly straightforward with the built in scatterplot method. We first import the pyplot class and seaborn library. We use the same DataFrame as earlier to visualize the clusters. To visualize the data points with each point associated with a separate color, we use the hue argument of the scatterplot method, and pass on the column name of the cluster labels, which is labels in this example. Now that we have written the code for each of them, let us compare the results. Recall from the last lesson that seaborn shows an extra cluster with label 0 if the cluster labels are integers. In this example, we have manually assigned string cluster labels, so this issue will not arise.

6. Comparison of both methods of visualization

Although the results are comparable, there are two reasons why we prefer seaborn. First, the implementation using seaborn was more convenient once you have stored cluster labels in your DataFrame. Second, you do not need to manually select colors in seaborn as it would be using a default palette no matter how many clusters you have.

7. Next up: Try some visualizations

Now that you know how to visualize data using two libraries, let us try some exercises.

This exercise is part of the course

Cluster Analysis in Python

IntermediateSkill Level

4.8+

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Before you are ready to classify news articles, you need to be introduced to the basics of clustering. This chapter familiarizes you with a class of machine learning algorithms called unsupervised learning and then introduces you to clustering, one of the popular unsupervised learning algorithms. You will know about two popular clustering techniques - hierarchical clustering and k-means clustering. The chapter concludes with basic pre-processing steps before you start clustering data.

Exercise 1: Unsupervised learning: basics Exercise 2: Unsupervised learning in real world Exercise 3: Pokémon sightings Exercise 4: Basics of cluster analysis Exercise 5: Pokémon sightings: hierarchical clustering Exercise 6: Pokémon sightings: k-means clustering Exercise 7: Data preparation for cluster analysis Exercise 8: Normalize basic list data Exercise 9: Visualize normalized data Exercise 10: Normalization of small numbers Exercise 11: FIFA 18: Normalize data

This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The chapter concludes with a discussion on the limitations of hierarchical clustering and discusses considerations while using hierarchical clustering.

Exercise 1: Basics of hierarchical clustering Exercise 2: Hierarchical clustering: ward method Exercise 3: Hierarchical clustering: single method Exercise 4: Hierarchical clustering: complete method Exercise 5: Visualize clusters

Current Exercise

Exercise 6: Visualize clusters with matplotlib Exercise 7: Visualize clusters with seaborn Exercise 8: How many clusters?Exercise 9: Create a dendrogram Exercise 10: How many clusters in comic con data?Exercise 11: Limitations of hierarchical clustering Exercise 12: Timing run of hierarchical clustering Exercise 13: FIFA 18: exploring defenders

This chapter introduces a different clustering algorithm - k-means clustering - and its implementation in SciPy. K-means clustering overcomes the biggest drawback of hierarchical clustering that was discussed in the last chapter. As dendrograms are specific to hierarchical clustering, this chapter discusses one method to find the number of clusters before running k-means clustering. The chapter concludes with a discussion on the limitations of k-means clustering and discusses considerations while using this algorithm.

Exercise 1: Basics of k-means clustering Exercise 2: K-means clustering: first exercise Exercise 3: Runtime of k-means clustering Exercise 4: How many clusters?Exercise 5: Elbow method on distinct clusters Exercise 6: Elbow method on uniform data Exercise 7: Limitations of k-means clustering Exercise 8: Impact of seeds on distinct clusters Exercise 9: Uniform clustering patterns Exercise 10: FIFA 18: defenders revisited

Now that you are familiar with two of the most popular clustering techniques, this chapter helps you apply this knowledge to real-world problems. The chapter first discusses the process of finding dominant colors in an image, before moving on to the problem discussed in the introduction - clustering of news articles. The chapter concludes with a discussion on clustering with multiple variables, which makes it difficult to visualize all the data.

Exercise 1: Dominant colors in images Exercise 2: Extract RGB values from image Exercise 3: How many dominant colors?Exercise 4: Display dominant colors Exercise 5: Document clustering Exercise 6: TF-IDF of movie plots Exercise 7: Top terms in movie clusters Exercise 8: Clustering with multiple features Exercise 9: Clustering with many features Exercise 10: Basic checks on clusters Exercise 11: FIFA 18: what makes a complete player?Exercise 12: Farewell!