Flat clustering (fcluster) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function for flat clustering from scipy.

SciPy

from scipy.cluster.hierarchy import [1]

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Alinkage

Bward

Cfcluster

Ddendrogram

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'linkage' instead of 'fcluster'.

Confusing 'dendrogram' with 'fcluster'.

The fcluster function is used to form flat clusters from hierarchical clustering linkage data.

2fill in blank

medium

Complete the code to create flat clusters using a distance threshold of 1.5.

SciPy

clusters = fcluster(linkage_matrix, [1], criterion='distance')

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A3.0

B2.0

C0.5

D1.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using a threshold other than 1.5.

Confusing the threshold with the number of clusters.

The threshold distance for forming clusters is set to 1.5 as specified.

3fill in blank

hard

Fix the error in the code to correctly assign clusters by maximum number of clusters = 3.

SciPy

clusters = fcluster(linkage_matrix, [1], criterion='maxclust')

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A3

B2

C4

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using a threshold that does not match the desired number of clusters.

Confusing 'distance' criterion with 'maxclust'.

To form exactly 3 clusters, the threshold parameter should be 3 with criterion 'maxclust'.

4fill in blank

hard

Fill both blanks to create a dictionary of cluster labels and their counts.

SciPy

cluster_counts = {label: [1] for label in set(clusters) if clusters.count(label) [2] 1}

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Aclusters.count(label)

B>

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '>' in the condition.

Using a wrong method to count occurrences.

The dictionary comprehension counts how many times each label appears and filters labels with counts greater than 1.

5fill in blank

hard

Fill all three blanks to create a list of cluster labels for points with distance less than 2.0.

SciPy

selected_clusters = [[1] for i, d in enumerate(distances) if d [2] [3]]

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Aclusters[i]

B<

C2.0

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' instead of '<' in the condition.

Using 'd' instead of 'clusters[i]' in the output.

This list comprehension selects cluster labels for points where the distance is less than 2.0.