Dendrogram visualization in SciPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a dendrogram in data science?

A dendrogram is a tree-like diagram that shows how data points group together in hierarchical clustering. It helps us see the order and distance of clusters.

beginner

Which Python library provides the dendrogram function for hierarchical clustering visualization?

The scipy.cluster.hierarchy module provides the dendrogram() function to create dendrogram visualizations.

intermediate

What input does the dendrogram function require?

It requires a linkage matrix, which encodes the hierarchical clustering information such as which clusters are merged and their distances.

intermediate

How can you interpret the height of the branches in a dendrogram?

The height of each branch shows the distance or dissimilarity between clusters when they merge. Taller branches mean clusters are more different.

intermediate

Name one way to customize a dendrogram plot in scipy.

You can customize colors, orientation, labels, and truncate the dendrogram to show only a part of the tree using parameters like color_threshold, orientation, and truncate_mode.

What does a dendrogram visualize?

A Linear regression results

B Hierarchical clustering of data points

C Time series trends

D Classification accuracy

A dendrogram shows the hierarchical clustering structure of data.

Which function creates a dendrogram in scipy?

A scipy.cluster.hierarchy.distance()

B scipy.cluster.hierarchy.linkage()

C scipy.cluster.hierarchy.fcluster()

D scipy.cluster.hierarchy.dendrogram()

The dendrogram() function plots the hierarchical clustering as a tree.

What does the height of a dendrogram branch represent?

A Distance between merged clusters

B Cluster label

C Time taken to cluster

D Number of data points in cluster

Branch height shows how far apart clusters are when merged.

What input is needed to plot a dendrogram?

A Cluster labels

B Raw data points

C Linkage matrix

D Distance matrix

The linkage matrix contains the hierarchical clustering info needed.

Which parameter controls the orientation of a dendrogram plot?

A orientation

B truncate_mode

C color_threshold

D leaf_rotation

The orientation parameter sets the dendrogram direction (top, left, right, bottom).

Explain how a dendrogram helps in understanding hierarchical clustering results.

Think about how the tree shows groups joining step by step.

Describe the steps to create a dendrogram plot using scipy.

Start from data, then cluster, then plot.

Practice

1. What is the main purpose of a dendrogram in data science?

easy

A. To visualize hierarchical clustering as a tree

B. To perform linear regression analysis

C. To calculate the mean of a dataset

D. To create a scatter plot of two variables

Solution

1. Step 1: Understand dendrogram function

   A dendrogram is used to show hierarchical clustering results visually as a tree structure.

2. Step 2: Compare with other options

   The other options describe different data analysis or visualization methods unrelated to dendrograms.

3. Final Answer:

   To visualize hierarchical clustering as a tree -> Option A

4. Quick Check:

   Dendrogram = hierarchical clustering tree [OK]

Hint: Dendrograms always show clusters as tree diagrams [OK]

Common Mistakes:

• Confusing dendrogram with scatter plot
• Thinking dendrogram calculates statistics
• Mixing dendrogram with regression plots

2. Which of the following is the correct way to import the dendrogram function from scipy?

easy

A. from scipy.visualization import dendrogram

B. import scipy.dendrogram

C. import dendrogram from scipy.cluster

D. from scipy.cluster.hierarchy import dendrogram

Solution

1. Step 1: Recall correct import syntax

   The dendrogram function is located in scipy.cluster.hierarchy, so the correct import is from scipy.cluster.hierarchy import dendrogram.

2. Step 2: Check other options for syntax errors

   The other options use incorrect module paths or invalid import syntax.

3. Final Answer:

   from scipy.cluster.hierarchy import dendrogram -> Option D

4. Quick Check:

   Correct import path = from scipy.cluster.hierarchy import dendrogram [OK]

Hint: Remember dendrogram is in scipy.cluster.hierarchy [OK]

Common Mistakes:

• Using wrong module path
• Incorrect import syntax
• Assuming dendrogram is in scipy.visualization

3. Given the following code, what will be the output type of dn?

from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np

X = np.array([[1, 2], [3, 4], [5, 6]])
Z = linkage(X, 'single')
dn = dendrogram(Z)

medium

A. A NumPy array of cluster labels

B. A dictionary containing dendrogram data

C. A matplotlib figure object

D. A list of linkage distances

Solution

1. Step 1: Understand dendrogram return value

   The dendrogram function returns a dictionary with keys like 'icoord', 'dcoord', 'leaves', and 'color_list' describing the dendrogram structure.

2. Step 2: Check other options

   A NumPy array of cluster labels is incorrect because cluster labels are not returned by dendrogram. A matplotlib figure object is wrong because dendrogram does not return a figure object. A list of linkage distances is incorrect as linkage distances are part of the linkage matrix, not dendrogram output.

3. Final Answer:

   A dictionary containing dendrogram data -> Option B

4. Quick Check:

   dendrogram() returns dict = A dictionary containing dendrogram data [OK]

Hint: dendrogram() returns a dict with plotting info [OK]

Common Mistakes:

• Expecting dendrogram to return a plot object
• Confusing dendrogram output with linkage matrix
• Thinking dendrogram returns cluster labels

4. Identify the error in this code snippet for plotting a dendrogram:

from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt

X = [[1, 2], [3, 4], [5, 6]]
Z = linkage(X, 'ward')
dendrogram(Z)
plt.show()

medium

A. Linkage method 'ward' is invalid

B. Missing import for numpy

C. No error; code runs and plots dendrogram correctly

D. X should be a NumPy array, not a list

Solution

1. Step 1: Check data input type

   Linkage accepts array-like input, so a Python list of lists is valid for X.

2. Step 2: Verify linkage method and plotting

   'ward' is a valid linkage method. The code imports matplotlib.pyplot as plt and calls plt.show(), so the dendrogram will plot correctly.

3. Final Answer:

   No error; code runs and plots dendrogram correctly -> Option C

4. Quick Check:

   List input and 'ward' method are valid [OK]

Hint: Linkage accepts lists; 'ward' is valid method [OK]

Common Mistakes:

• Assuming input must be NumPy array
• Thinking 'ward' is invalid linkage method
• Forgetting plt.show() to display plot

5. You want to visualize clusters with different colors in a dendrogram using scipy.cluster.hierarchy.dendrogram. Which parameter should you set to control the color threshold for cluster coloring?

hard

A. color_threshold

B. linkage_method

C. leaf_rotation

D. distance_metric

Solution

1. Step 1: Identify parameter for cluster color control

   The parameter color_threshold in dendrogram controls the threshold distance to color clusters differently.

2. Step 2: Eliminate unrelated parameters

   linkage_method and distance_metric relate to clustering, not coloring. leaf_rotation controls label rotation, not colors.

3. Final Answer:

   color_threshold -> Option A

4. Quick Check:

   Cluster colors controlled by color_threshold [OK]

Hint: Use color_threshold to set cluster color boundaries [OK]

Common Mistakes:

• Confusing color_threshold with linkage method
• Using leaf_rotation to change colors
• Assuming distance_metric affects dendrogram colors