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Implementation of Agglomerative Hierarchical Clustering in TensorFlow

Agglomerative Hierarchical Clustering is a widely used clustering technique that builds a hierarchy of clusters by iteratively merging the nearest clusters. While TensorFlow is not traditionally used for hierarchical clustering, we can still leverage its capabilities for implementing custom clustering techniques. In this article, we’ll demonstrate how to implement Agglomerative Hierarchical Clustering using TensorFlow.

1. Introduction to Custom Clustering in TensorFlow​

TensorFlow provides the flexibility to implement custom machine learning algorithms. Although it doesn't have built-in functions for hierarchical clustering like Scikit-Learn, we can still create custom implementations using TensorFlow's powerful array operations.

2. Step-by-Step Guide to Implementing Agglomerative Clustering​

2.1 Importing Necessary Libraries​

We will start by importing the required libraries, including TensorFlow:

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

2.2 Generating a Synthetic Dataset​

Let’s create a synthetic dataset similar to what we used in the Scikit-Learn implementation:

# Generate synthetic data
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=150, centers=3, cluster_std=0.6, random_state=42)

# Plot the data
plt.scatter(X[:, 0], X[:, 1], c='blue', marker='o', edgecolor='k')
plt.title("Generated Data")
plt.show()

2.3 Custom Implementation of Agglomerative Clustering in TensorFlow​

In TensorFlow, we can implement Agglomerative Clustering by computing pairwise distances, identifying clusters to merge, and iteratively updating the distance matrix.

def pairwise_distances(X):
    """ Compute the pairwise Euclidean distance between points. """
    expanded_a = tf.expand_dims(X, 0)
    expanded_b = tf.expand_dims(X, 1)
    distances = tf.reduce_sum(tf.square(expanded_a - expanded_b), 2)
    return tf.sqrt(distances)

def agglomerative_clustering(X, n_clusters):
    """ Implement a basic version of Agglomerative Clustering. """
    distances = pairwise_distances(X)
    num_points = tf.shape(X)[0]

    clusters = {i: [i] for i in range(num_points.numpy())}

    while len(clusters) > n_clusters:
        min_dist = np.inf
        to_merge = None

        for i in clusters:
            for j in clusters:
                if i != j:
                    d = np.mean([distances[a, b] for a in clusters[i] for b in clusters[j]])
                    if d < min_dist:
                        min_dist = d
                        to_merge = (i, j)

        clusters[to_merge[0]].extend(clusters[to_merge[1]])
        del clusters[to_merge[1]]

    return clusters

# Apply Agglomerative Clustering with TensorFlow
clusters_tf = agglomerative_clustering(tf.constant(X, dtype=tf.float32), n_clusters=3)

2.4 Visualizing the Clustering Results​

Now, let’s visualize the clustering results by assigning a unique color to each cluster.

# Assign cluster labels
labels = np.zeros(X.shape[0], dtype=int)
for cluster_id, points in enumerate(clusters_tf.values()):
    for point in points:
        labels[point] = cluster_id

# Plot the clustering result
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', marker='o', edgecolor='k')
plt.title("Agglomerative Clustering with TensorFlow")
plt.show()

2.5 Visualizing the Dendrogram​

Although TensorFlow does not directly support creating dendrograms, we can still use SciPy to visualize the hierarchical clustering structure:

# Create a linkage matrix using SciPy's linkage function
linked = linkage(X, method='ward')

# Plot the dendrogram
plt.figure(figsize=(10, 7))
dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True)
plt.title("Dendrogram")
plt.show()

2.6 Comparing Results with Scikit-Learn​

To ensure the correctness of our TensorFlow implementation, it’s useful to compare the results with those obtained from Scikit-Learn's AgglomerativeClustering class.

3. Conclusion​

In this article, we demonstrated how to implement Agglomerative Hierarchical Clustering using TensorFlow. While TensorFlow may not be the first choice for hierarchical clustering, its flexibility allows us to create custom implementations that can be tailored to specific needs.

With TensorFlow's powerful array operations, you can extend the basic implementation presented here to include more advanced features, such as different linkage criteria and custom distance metrics. Next, we’ll explore implementing Agglomerative Clustering using PyTorch.