Clustering
What is Clustering?
Unsupervised machine learning fundamentally includes the clustering of comparable data points based on their inherent properties. Finding patterns or structures within the data without the use of labels or prior knowledge is the aim of clustering.
Data points as clusters
Typically, a dataset for clustering is made up of a number of data points or observations. Each data point is represented by a collection of characteristics or attributes, which may be categorized or numerical. The goal is to divide the data points into separate groups or clusters, where those within the same cluster are more similar to one another than those in different clusters. Usually, a distance metric, like Euclidean distance or cosine similarity, is used to describe how similar or unlike two data points are.
Finding a suitable representation for the data and establishing a standard for gauging how similar the data points are to one another are two steps in the clustering process. Clustering can be done using a number of algorithms and methods, such as:
1. K-means: This algorithm divides the data into K clusters, where K is a fixed number. It updates the centroids until convergence and iteratively allocates data points to the nearest cluster centroid based on distance.
2. Hierarchical clustering: By iteratively combining or dividing clusters according to their similarity, the hierarchical clustering method creates a hierarchy of clusters. It can be agglomerative, starting with each data point as a separate cluster and then merging the most comparable clusters, or divisive, starting with all the data points in a single cluster and recursively separating them.
3. Density-based Clustering: Based on a density threshold, algorithms like DBSCAN (Density-Based Spatial Clustering of Applications with Noise) put densely connected data points together. It describes clusters as high-density zones divided by lower-density sections.
4. GMMs (Gaussian Mixture Models): GMMs presumptively construct the data points from a combination of Gaussian distributions. It distributes data points to the most likely cluster and estimates the Gaussian components' parameters using the Expectation-Maximization technique.
5. Spectrum clustering: The groups of data points by utilizing the spectrum characteristics of a similarity or affinity matrix. A dimensionality reduction stage is completed, and the reduced space is then subjected to a clustering process.
Depending on the type of data, desired cluster characteristics (such as compactness or connection), and computing needs, a clustering algorithm is chosen. Metrics like silhouette score or cluster purity are frequently used to gauge the caliber of clustering findings.
Why to use the clustering concept?
The clustering concept has a number of advantages, which is why it is used in a variety of applications. Here are some main explanations for why clustering is so popular:
- Pattern Analysis: Clustering aids in the discovery of underlying patterns, structures, or correlations in a collection. In order to show clusters or cluster characteristics, similar data points are grouped together. These clusters or characteristics may be related to significant categories or classes in the data. By using clustering, the data can be understood more clearly and its underlying organization can be shown.
- Data exploration and visualization: By combining related data points into a single group, clustering creates a concise representation of the data. As a result, data exploration and visualization are made possible, enabling analysts to get a broad perspective and understand complex datasets. Clustered data can be represented using visualization techniques like scatter plots, heatmaps, or dendrograms, which can help with data understanding.
- Data preparation: For a number of machine learning tasks, clustering can be employed as a preprocessing step. By substituting numerous associated variables with a single cluster label or with representative features obtained during the clustering process, it aids in reducing the dimensionality of the data. This reduction in dimensions might make the subsequent analysis simpler and boost computational performance.
- Data Compression and Storage: Clustering allows for data compression by using fewer cluster centroids to represent a group of related data points. In large-scale applications or situations with limited storage capacity, this compression minimizes the amount of space needed to store the data, making it more effective for storage and retrieval.
- Decision-Making and Insights: By classifying data into useful groupings, clustering makes decision-making easier. It enables focused analysis and group- or cluster-specific decision-making. For instance, clustering in marketing can assist in determining consumer categories for individualized marketing campaigns. Using clustering makes it easier to extract useful information, spot trends, and make decisions that are based on the traits of each cluster.
Clustering with Deep Learning Models
Neural network designs are used to carry out clustering tasks in deep learning models. Due to their capacity to automatically learn hierarchical representations and identify intricate patterns in the data, deep learning models have become more popular in the clustering community.
The following are a few methods for clustering using deep learning models:
- Autoencoders are neural networks that have been taught to recreate the input data. They are made up of an encoder that converts the input data into a latent space with fewer dimensions and a decoder that reconstructs the input data from the latent representation. You can conduct clustering by using the latent space gained by training an autoencoder on unlabeled data. Traditional clustering algorithms, like K-means or Gaussian Mixture Models, can be used to accomplish this by applying them to the retrieved data.
- Deep Embedded Clustering (DEC): DEC blends clustering and deep autoencoders. By adding a clustering loss that motivates the network to learn representations appropriate for clustering, it expands on the concept of autoencoders. Cluster assignments and network weights are iteratively updated during DEC till convergence. In this method, the clustering assignment and the deep representation are concurrently learned and then optimized.
- Variational Autoencoders (VAEs): VAEs are generative models that train a probabilistic representation of the input data in latent space. By estimating the posterior distribution of cluster assignments for each data point, clustering can be done by utilizing the probabilistic character of VAEs. The Gaussian Mixture Variational Autoencoder (GMVAE), which simulates the latent space as a combination of Gaussian distributions, is one method.
- Self-Organizing Maps (SOM): Based on commonalities between data points, SOMs are neural network designs that arrange data points into a low-dimensional grid. Deep learning methods can be used to train SOMs, and they have been effectively used for clustering tasks. They offer a grid-based depiction of the clusters to make interpretation and analysis simple.
- Deep Clustering with Joint Optimization: In this method, the cluster assignments and the deep neural network parameters are both optimized. It combines grouping goals with deep learning models like convolutional neural networks (CNNs) or recurrent neural networks (RNNs). By maximizing a joint loss function, the network is trained to concurrently learn meaningful representations and cluster assignments.
In order to learn complicated representations and find underlying patterns in the data, these methods make use of the capability of deep learning models. Deep learning-based clustering techniques can, however, be computationally expensive and require a significant amount of training data. Additionally, it can be difficult to comprehend the results and choose the right architectures and hyperparameters. To evaluate the effectiveness of deep learning-based clustering models on particular datasets, careful examination and comparison with conventional clustering methods are required.
Implementation
Dataset: MNIST
Platform: Colaboratory
Source code
!pip install tensorflow numpy matplotlib scikit-learn# Import the required libraries
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# Normalize pixel values
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# Flatten images
x_train = x_train.reshape((-1, 784))
x_test = x_test.reshape((-1, 784))
# Define the deep autoencoder model
input_dim = x_train.shape[1]
encoding_dim = 32
input_layer = tf.keras.layers.Input(shape=(input_dim,))
encoder = tf.keras.layers.Dense(encoding_dim, activation='relu')(input_layer)
decoder = tf.keras.layers.Dense(input_dim, activation='sigmoid')(encoder)
autoencoder = tf.keras.models.Model(inputs=input_layer, outputs=decoder)
# Compile and train the autoencoder
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train, epochs=5, batch_size=256, shuffle=True, validation_data=(x_test, x_test))
# Extract latent representations
encoder_model = tf.keras.models.Model(inputs=input_layer, outputs=encoder)
latent_representations = encoder_model.predict(x_test)
# Perform clustering using K-means
num_clusters = 10
kmeans = KMeans(n_clusters=num_clusters)
cluster_labels = kmeans.fit_predict(latent_representations)
# Visualize original and clustered images
fig, axes = plt.subplots(num_clusters, 10, figsize=(10, 10))
for cluster in range(num_clusters):
cluster_indices = np.where(cluster_labels == cluster)[0][:10]
for idx, image_idx in enumerate(cluster_indices):
axes[cluster, idx].imshow(x_test[image_idx].reshape(28, 28), cmap='gray')
axes[cluster, idx].axis('off')
plt.tight_layout()
plt.show()
Epoch 1/5
235/235 [==============================] - 4s 12ms/step - loss: 0.2743 - val_loss: 0.1857
Epoch 2/5
235/235 [==============================] - 2s 10ms/step - loss: 0.1687 - val_loss: 0.1525
Epoch 3/5
235/235 [==============================] - 3s 11ms/step - loss: 0.1441 - val_loss: 0.1344
Epoch 4/5
235/235 [==============================] - 3s 11ms/step - loss: 0.1292 - val_loss: 0.1220
Epoch 5/5
235/235 [==============================] - 4s 15ms/step - loss: 0.1191 - val_loss: 0.1139
313/313 [==============================] - 0s 1ms/step
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Clustered images and original image visualization
- The program will produce a grid of pictures that includes both the clustered pictures and the original grayscale pictures from the MNIST dataset.
- The clusters' first 10 photographs will be displayed in each row of the grid, which has rows for each cluster.
- A grayscale color map will be used to display the photos, showing the pixel intensity.
- The score for Normalized Mutual Information (NMI) and the code calculates the NMI between the cluster labels derived by K-means and the true labels (y_test), however, it does not directly present the NMI score.
- By assessing the similarity between the genuine labels and the cluster assignments, the NMI score offers an assessment of the clustering's quality.
- Better alignment between the true labels and the clusters is indicated by a higher NMI score (closer to 1), which also suggests better clustering performance.
- The grid of photos depicting the original photographs and their related clustered images will be visible after you run the code. Understanding how the clustering algorithm puts related photos together based on their latent representations is made possible by this graphic.
- The NMI score, which represents the degree of similarity between the genuine labels and the cluster assignments, will also be presented in the console. A higher NMI score indicates better clustering performance and aids in evaluating the quality of the clustering findings.
- You may learn more about how successfully the clustering algorithm separates and puts related photos together based on their learned representations by looking at the images and taking the NMI score into account.
Key Points to Remember
- It is essential to prepare and normalize the data in order to achieve relevant clustering findings.
- For your clustering problem, pick an appropriate deep-learning model architecture, such as autoencoders, VAEs, or SOMs.
- The encoder portion of the model's latent space representation should include distinguishing and significant features.
- Create a suitable loss function for the deep learning model's training, which usually entails recreating the input data.
- To assign cluster labels, use a suitable clustering technique, such as K-means, GMMs, agglomerative clustering, or spectral clustering.
- Utilize metrics such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), or Silhouette Score to assess the caliber of clustering.
- Try adjusting the hyperparameters, such as the learning rate, batch size, or number of clusters, to improve the clustering performance.
- Interpret the clustered patterns and acquire an understanding of the model's grouping behavior by visualizing the results.
- Deep learning models can be computationally expensive, thus taking into account the computing needs and resources that are accessible.
- Compare the effectiveness of deep learning-based clustering techniques to baselines using conventional clustering algorithms.
- To determine the best strategy for your unique clustering assignment, adapt and test out several strategies.
- By keeping these things in mind, you may increase the precision and readability of your results as well as your comprehension of clustering with deep learning models.
Conclusion
For unsupervised learning tasks, clustering with deep learning models is a potent method that can find hidden patterns and group related data points together. It is feasible to take meaningful representations out of the data and execute clustering based on these representations by utilizing deep learning architectures like autoencoders, VAEs, or SOMs.
Appropriate data preparation, choosing appropriate model architectures, careful design of loss functions, application of clustering algorithms, evaluation of clustering performance using metrics, and consideration of computational requirements are important things to keep in mind when working with clustering and deep learning models.
Complex associations in the data can be captured using deep learning-based clustering, but it also needs careful parameter evaluation and adjustment. Results visualization and comparison with conventional clustering techniques might offer insightful validation.
The ultimate objective is to achieve precise and understandable clustering findings, which can help with jobs like customer segmentation, picture analysis, anomaly detection, and many other things. You may enhance the performance of clustering using deep learning models for your particular application by continuously experimenting and exploring new methods.
Reference
