I have an array of mouse coordinates/movements at different times. I want to now the interesting parts of the screen the user hovered over; the clusters. How do I calculate those?
  Calculating interesting parts of the screen, also known as clusters or hotspots, from mouse coordinates/movements can be done using a variety of techniques. Here are some common approaches:

1. K-Means Clustering:
K-Means clustering is a popular unsupervised learning algorithm that can be used to group similar data points into clusters. In your case, you can apply K-Means clustering to the mouse coordinates to identify clusters of similar mouse movements. The algorithm works by defining K clusters and assigning each data point to the cluster with the closest mean distance. You can use a library like scikit-learn to implement K-Means clustering in Python.
2. Hierarchical Clustering:
Hierarchical clustering is another unsupervised learning technique that can be used to group data points into clusters. This algorithm builds a hierarchy of clusters by merging or splitting existing clusters. You can use a library like scipy to implement hierarchical clustering in Python.
3. DBSCAN Clustering:
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that can be used to identify clusters of varying densities. DBSCAN works by defining a neighborhood around each data point and then grouping together points that are close to each other in the neighborhood. You can use a library like sci
I have an array of mouse coordinates/movements at different times. I want to now the interesting parts of the screen the user hovered over; the clusters. How do I calculate those?

There are many ways to identify clusters in your data, but one common method is using density-based clustering. In this method, the algorithm groups together points that are close to each other and have a high density of points around them, while separating points that are far apart or have a low density of points around them.

One popular algorithm for density-based clustering is DBSCAN (Density-Based Spatial Clustering of Applications with Noise). DBSCAN requires two parameters: `eps` (the maximum distance between two samples for them to be considered as in the same neighborhood) and `MinPts` (the minimum number of samples in a neighborhood for a dense region).

Here's a simplified example of how you can use DBSCAN to find clusters in your data using the scikit-learn library in Python:

```python
from sklearn.cluster import DBSCAN
import numpy as np

# Assuming you have an array of mouse coordinates (x, y)
mouse_coordinates = np.array([[x1, y1], [x2, y2], ..., [xn, yn]])

# Set the parameters for DBSCAN
eps = 50  # Maximum distance between two samples
MinPts = 5  # Minimum number of samples in a neighborhood for a dense region

# Create a