Time Series Clustering

Aeon provides clustering algorithms adapted for temporal data with specialized distance metrics and averaging methods.

Partitioning Algorithms

Standard k-means/k-medoids adapted for time series:

TimeSeriesKMeans - K-means with temporal distance metrics (DTW, Euclidean, etc.)
TimeSeriesKMedoids - Uses actual time series as cluster centers
TimeSeriesKShape - Shape-based clustering algorithm
TimeSeriesKernelKMeans - Kernel-based variant for nonlinear patterns

Use when: Known number of clusters, spherical cluster shapes expected.

Large Dataset Methods

Efficient clustering for large collections:

TimeSeriesCLARA - Clustering Large Applications with sampling
TimeSeriesCLARANS - Randomized search variant of CLARA

Use when: Dataset too large for standard k-medoids, need scalability.

Elastic Distance Clustering

Specialized for alignment-based similarity:

KASBA - K-means with shift-invariant elastic averaging
ElasticSOM - Self-organizing map using elastic distances

Use when: Time series have temporal shifts or warping.

Spectral Methods

Graph-based clustering:

KSpectralCentroid - Spectral clustering with centroid computation

Use when: Non-convex cluster shapes, need graph-based approach.

Deep Learning Clustering

Neural network-based clustering with auto-encoders:

AEFCNClusterer - Fully convolutional auto-encoder
AEResNetClusterer - Residual network auto-encoder
AEDCNNClusterer - Dilated CNN auto-encoder
AEDRNNClusterer - Dilated RNN auto-encoder
AEBiGRUClusterer - Bidirectional GRU auto-encoder
AEAttentionBiGRUClusterer - Attention-enhanced BiGRU auto-encoder

Use when: Large datasets, need learned representations, or complex patterns.

Feature-Based Clustering

Transform to feature space before clustering:

Catch22Clusterer - Clusters on 22 canonical features
SummaryClusterer - Uses summary statistics
TSFreshClusterer - Automated tsfresh features

Use when: Raw time series not informative, need interpretable features.

Composition

Build custom clustering pipelines:

ClustererPipeline - Chain transformers with clusterers

Averaging Methods

Compute cluster centers for time series:

mean_average - Arithmetic mean
ba_average - Barycentric averaging with DTW
kasba_average - Shift-invariant averaging
shift_invariant_average - General shift-invariant method

Use when: Need representative cluster centers for visualization or initialization.

Quick Start

from aeon.clustering import TimeSeriesKMeans
from aeon.datasets import load_classification

# Load data (using classification data for clustering)
X_train, _ = load_classification("GunPoint", split="train")

# Cluster time series
clusterer = TimeSeriesKMeans(
    n_clusters=3,
    distance="dtw",  # Use DTW distance
    averaging_method="ba"  # Barycentric averaging
)
labels = clusterer.fit_predict(X_train)
centers = clusterer.cluster_centers_

Algorithm Selection

Speed priority: TimeSeriesKMeans with Euclidean distance
Temporal alignment: KASBA, TimeSeriesKMeans with DTW
Large datasets: TimeSeriesCLARA, TimeSeriesCLARANS
Complex patterns: Deep learning clusterers
Interpretability: Catch22Clusterer, SummaryClusterer
Non-convex clusters: KSpectralCentroid

Distance Metrics

Compatible distance metrics include: - Euclidean, Manhattan, Minkowski (lock-step) - DTW, DDTW, WDTW (elastic with alignment) - ERP, EDR, LCSS (edit-based) - MSM, TWE (specialized elastic)

Evaluation

Use clustering metrics from sklearn or aeon benchmarking: - Silhouette score - Davies-Bouldin index - Calinski-Harabasz index