Comparing different clustering algorithms on toy datasets

This example shows characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D. With the exception of the last dataset, the parameters of each of these dataset-algorithm pairs has been tuned to produce good clustering results. Some algorithms are more sensitive to parameter values than others.

The last dataset is an example of a ‘null’ situation for clustering: the data is homogeneous, and there is no good clustering. For this example, the null dataset uses the same parameters as the dataset in the row above it, which represents a mismatch in the parameter values and the data structure.

While these examples give some intuition about the algorithms, this intuition might not apply to very high dimensional data.

MiniBatch KMeans, Affinity Propagation, MeanShift, Spectral Clustering, Ward, Agglomerative Clustering, DBSCAN, HDBSCAN, OPTICS, BIRCH, Gaussian Mixture
import time
import warnings
from itertools import cycle, islice

import matplotlib.pyplot as plt
import numpy as np

from sklearn import cluster, datasets, mixture
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler

# ============
# Generate datasets. We choose the size big enough to see the scalability
# of the algorithms, but not too big to avoid too long running times
# ============
n_samples = 500
seed = 30
noisy_circles = datasets.make_circles(
    n_samples=n_samples, factor=0.5, noise=0.05, random_state=seed
)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05, random_state=seed)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=seed)
rng = np.random.RandomState(seed)
no_structure = rng.rand(n_samples, 2), None

# Anisotropicly distributed data
random_state = 170
X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)
transformation = [[0.6, -0.6], [-0.4, 0.8]]
X_aniso = np.dot(X, transformation)
aniso = (X_aniso, y)

# blobs with varied variances
varied = datasets.make_blobs(
    n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state
)

# ============
# Set up cluster parameters
# ============
plt.figure(figsize=(9 * 2 + 3, 13))
plt.subplots_adjust(
    left=0.02, right=0.98, bottom=0.001, top=0.95, wspace=0.05, hspace=0.01
)

plot_num = 1

default_base = {
    "quantile": 0.3,
    "eps": 0.3,
    "damping": 0.9,
    "preference": -200,
    "n_neighbors": 3,
    "n_clusters": 3,
    "min_samples": 7,
    "xi": 0.05,
    "min_cluster_size": 0.1,
    "allow_single_cluster": True,
    "hdbscan_min_cluster_size": 15,
    "hdbscan_min_samples": 3,
    "random_state": 42,
}

datasets = [
    (
        noisy_circles,
        {
            "damping": 0.77,
            "preference": -240,
            "quantile": 0.2,
            "n_clusters": 2,
            "min_samples": 7,
            "xi": 0.08,
        },
    ),
    (
        noisy_moons,
        {
            "damping": 0.75,
            "preference": -220,
            "n_clusters": 2,
            "min_samples": 7,
            "xi": 0.1,
        },
    ),
    (
        varied,
        {
            "eps": 0.18,
            "n_neighbors": 2,
            "min_samples": 7,
            "xi": 0.01,
            "min_cluster_size": 0.2,
        },
    ),
    (
        aniso,
        {
            "eps": 0.15,
            "n_neighbors": 2,
            "min_samples": 7,
            "xi": 0.1,
            "min_cluster_size": 0.2,
        },
    ),
    (blobs, {"min_samples": 7, "xi": 0.1, "min_cluster_size": 0.2}),
    (no_structure, {}),
]

for i_dataset, (dataset, algo_params) in enumerate(datasets):
    # update parameters with dataset-specific values
    params = default_base.copy()
    params.update(algo_params)

    X, y = dataset

    # normalize dataset for easier parameter selection
    X = StandardScaler().fit_transform(X)

    # estimate bandwidth for mean shift
    bandwidth = cluster.estimate_bandwidth(X, quantile=params["quantile"])

    # connectivity matrix for structured Ward
    connectivity = kneighbors_graph(
        X, n_neighbors=params["n_neighbors"], include_self=False
    )
    # make connectivity symmetric
    connectivity = 0.5 * (connectivity + connectivity.T)

    # ============
    # Create cluster objects
    # ============
    ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
    two_means = cluster.MiniBatchKMeans(
        n_clusters=params["n_clusters"],
        random_state=params["random_state"],
    )
    ward = cluster.AgglomerativeClustering(
        n_clusters=params["n_clusters"], linkage="ward", connectivity=connectivity
    )
    spectral = cluster.SpectralClustering(
        n_clusters=params["n_clusters"],
        eigen_solver="arpack",
        affinity="nearest_neighbors",
        random_state=params["random_state"],
    )
    dbscan = cluster.DBSCAN(eps=params["eps"])
    hdbscan = cluster.HDBSCAN(
        min_samples=params["hdbscan_min_samples"],
        min_cluster_size=params["hdbscan_min_cluster_size"],
        allow_single_cluster=params["allow_single_cluster"],
    )
    optics = cluster.OPTICS(
        min_samples=params["min_samples"],
        xi=params["xi"],
        min_cluster_size=params["min_cluster_size"],
    )
    affinity_propagation = cluster.AffinityPropagation(
        damping=params["damping"],
        preference=params["preference"],
        random_state=params["random_state"],
    )
    average_linkage = cluster.AgglomerativeClustering(
        linkage="average",
        metric="cityblock",
        n_clusters=params["n_clusters"],
        connectivity=connectivity,
    )
    birch = cluster.Birch(n_clusters=params["n_clusters"])
    gmm = mixture.GaussianMixture(
        n_components=params["n_clusters"],
        covariance_type="full",
        random_state=params["random_state"],
    )

    clustering_algorithms = (
        ("MiniBatch\nKMeans", two_means),
        ("Affinity\nPropagation", affinity_propagation),
        ("MeanShift", ms),
        ("Spectral\nClustering", spectral),
        ("Ward", ward),
        ("Agglomerative\nClustering", average_linkage),
        ("DBSCAN", dbscan),
        ("HDBSCAN", hdbscan),
        ("OPTICS", optics),
        ("BIRCH", birch),
        ("Gaussian\nMixture", gmm),
    )

    for name, algorithm in clustering_algorithms:
        t0 = time.time()

        # catch warnings related to kneighbors_graph
        with warnings.catch_warnings():
            warnings.filterwarnings(
                "ignore",
                message="the number of connected components of the "
                + "connectivity matrix is [0-9]{1,2}"
                + " > 1. Completing it to avoid stopping the tree early.",
                category=UserWarning,
            )
            warnings.filterwarnings(
                "ignore",
                message="Graph is not fully connected, spectral embedding"
                + " may not work as expected.",
                category=UserWarning,
            )
            algorithm.fit(X)

        t1 = time.time()
        if hasattr(algorithm, "labels_"):
            y_pred = algorithm.labels_.astype(int)
        else:
            y_pred = algorithm.predict(X)

        plt.subplot(len(datasets), len(clustering_algorithms), plot_num)
        if i_dataset == 0:
            plt.title(name, size=18)

        colors = np.array(
            list(
                islice(
                    cycle(
                        [
                            "#377eb8",
                            "#ff7f00",
                            "#4daf4a",
                            "#f781bf",
                            "#a65628",
                            "#984ea3",
                            "#999999",
                            "#e41a1c",
                            "#dede00",
                        ]
                    ),
                    int(max(y_pred) + 1),
                )
            )
        )
        # add black color for outliers (if any)
        colors = np.append(colors, ["#000000"])
        plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])

        plt.xlim(-2.5, 2.5)
        plt.ylim(-2.5, 2.5)
        plt.xticks(())
        plt.yticks(())
        plt.text(
            0.99,
            0.01,
            ("%.2fs" % (t1 - t0)).lstrip("0"),
            transform=plt.gca().transAxes,
            size=15,
            horizontalalignment="right",
        )
        plot_num += 1

plt.show()

Total running time of the script: (0 minutes 5.560 seconds)

Related examples

Comparing different hierarchical linkage methods on toy datasets

Comparing different hierarchical linkage methods on toy datasets

Demo of HDBSCAN clustering algorithm

Demo of HDBSCAN clustering algorithm

A demo of structured Ward hierarchical clustering on an image of coins

A demo of structured Ward hierarchical clustering on an image of coins

Agglomerative clustering with and without structure

Agglomerative clustering with and without structure

Hierarchical clustering: structured vs unstructured ward

Hierarchical clustering: structured vs unstructured ward

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