Hands-On Exploratory Data Analysis with Python

Plotting clusters

Now, let's plot the clusters on the graph. We will start plotting using Principal Component Analysis (PCA) since it is good at capturing the global structure of the data. Then, we will use t-Distributed Stochastic Neighbor Embedding (TSNE) to plot the graph as it is good at capturing the relationship with the neighbors. Let's get started:

1. Let's start by creating the model again:

clusters = MiniBatchKMeans(n_clusters=4, init_size=1024, batch_size=2048, random_state=20).fit_predict(text)

2. Let's plot both graphs. First, we will plot using the PCA technique and then using the TSNE technique. Use the following code to do so:

max_label = max(clusters)max_items = np.random.choice(range(text.shape[0]), size=3000, replace=True) ...