What are some advanced clustering techniques in TensorFlow?

What are some advanced clustering techniques in TensorFlow?

TensorFlow offers several advanced clustering techniques, such as K-means, DBSCAN, and hierarchical clustering. Would you like to know more about a specific technique?

Can you explain how K-means works in TensorFlow?

K-means clustering partitions data into K distinct clusters. In TensorFlow, you can use the tf.keras API to implement it. Would you like a code example?

Yes, please provide a code example for K-means.

Here's a simple example:

import tensorflow as tf
from sklearn.datasets import make_blobs

# Generate sample data
X, _ = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

# Create K-means model
kmeans = tf.keras.layers.KMeans(n_clusters=4)

# Fit the model
kmeans.fit(X)

# Predict clusters
predictions = kmeans.predict(X)

This code generates sample data and applies K-means clustering. Do you have any questions about it?

What are the limitations of K-means clustering?

K-means has several limitations, including sensitivity to initial centroid placement, difficulty with non-spherical clusters, and the need to specify the number of clusters in advance. Would you like to explore alternatives?