About
Time Series Clustering is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series.
Source: Comprehensive Process Drift Detection with Visual Analytics
Benchmarks
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Greatest papers with code
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs.
DEEP CLUSTERING REPRESENTATION LEARNING TIME SERIES TIME SERIES CLUSTERING TIME SERIES FORECASTING
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
ICLR 2019
• ratschlab/SOM-VAE
• 
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
DIMENSIONALITY REDUCTION REPRESENTATION LEARNING TIME SERIES TIME SERIES CLUSTERING
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
• rymc/n2d
•
We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters.
Ranked #1 on
Image Clustering
on pendigits
DEEP CLUSTERING IMAGE CLUSTERING REPRESENTATION LEARNING TIME SERIES TIME SERIES CLUSTERING
Time Series Clustering via Community Detection in Networks
In this paper, we propose a technique for time series clustering using community detection in complex networks.
COMMUNITY DETECTION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLUSTERING
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e. g., adverse events, the onset of comorbidities).
Algorithms for Learning Graphs in Financial Markets
In the past two decades, the field of applied finance has tremendously benefited from graph theory.
Discovering patterns of online popularity from time series
By clustering the multidimensional time-series of the popularity of contents coupled with other domain-specific dimensions, we uncover two main patterns of popularity: bursty and steady temporal behaviors.
Learning Representations for Time Series Clustering
When applying seq2seq to time series clustering, obtaining a representation that effectively represents the temporal dynamics of the sequence, multi-scale features, and good clustering properties remains a challenge.
ANOMALY DETECTION REPRESENTATION LEARNING TIME SERIES TIME SERIES CLUSTERING
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation
The FunCLBM model extends the recently proposed Functional Latent Block Model and allows to create a dependency structure between row and column clusters.
AUTONOMOUS DRIVING DIMENSIONALITY REDUCTION FEATURE SELECTION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLUSTERING
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation
We propose a simple and efficient time-series clustering framework particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information.