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The points appear to be grouped into clusters, likely due to some noise or outlier removal process. One approach to cleaning up this data is by using a DBSCAN algorithm. Here's how you could use Python with Scikit-learn and Matplotlib for the same: ```python import matplotlib.pyplot as plt from sklearn.cluster import DBSCAN import numpy as np # Data points - note that it seems some duplicates might exist, so it makes sense to filter out data_points = [[-7.260273972602739,-21.506849315068493],[-8.082191780821917,-20.410958904109588],[-4.2465753424657535,-23.15068493150685],[-3.168485075238582,0.48375947538376045],[-1.4465974268193136,-11.94659315547224],[2.670822053680823,-15.37811831807837],[-2.0821917808219175,-17.945205479452056],[5.616438356164384,-13.561643835616438],[-10.547945205479452,-9.726027397260275]] # Transforming to 3d X = np.array(data_points) Z, X, Y = (x[0] for x in zip(*X)), (x[1] for x in zip(*X)), np.arange(X.shape[0]) from sklearn.datasets import make_blobs blobs, y = make_blobs(n_samples=50, centers=X, cluster_std=5, n_features=3, center_box=(-10.0, 10.0), random_state=1) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_scaled = scaler.fit_transform(X.reshape(-1, 2)).astype(np.float32) # Define the epsilon and min_samples parameters for DBSCAN algorithm epsilon = 4.5 # in case there's not too much variation (else would be something close to maximum point difference) min_samples = 6 db = DBSCAN(eps=epsilon, min_samples=min_samples).fit(X_scaled.reshape(-1,2)) core_samples_mask = np.array(db.core_sample_indices_ == -1) labels = db.labels_ # Plot the points plt.scatter(X[:,0], X[:,1]) # Print some labels for better visual understanding # Create a colormap and label it with values in `label_colors` (will get these using unique_labels, we could also write some if conditions here) for index, label_value in enumerate(labels): plt.annotate(label_value,index) print("Unique labels:", set(labels)) unique_labels = set(labels) plt.scatter(X[db.labels_ == 0 ,0], X[db.labels_ == 0,1]) plt.scatter(X[db.labels_ == -1 ,0], X[db.labels_ == -1,1]) plt.show() ``` This code will separate your data points into clusters and mark their labels as different colored markers. Note: This script requires matplotlib, numpy and scikit-learn packages. You can install these with pip by using: ``` pip install numpy pip install scikit-learn ```