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

3 Oct 2019ratschlab/SOM-VAE

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

16 Aug 2019rymc/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.

DEEP CLUSTERING IMAGE CLUSTERING REPRESENTATION LEARNING TIME SERIES TIME SERIES CLUSTERING

Time Series Clustering via Community Detection in Networks

19 Aug 2015lnferreira/time_series_clustering_via_community_detection

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

ICML 2020 chl8856/AC_TPC

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).

DECISION MAKING TIME SERIES TIME SERIES CLUSTERING

Algorithms for Learning Graphs in Financial Markets

31 Dec 2020mirca/fingraph

In the past two decades, the field of applied finance has tremendously benefited from graph theory.

GRAPH LEARNING TIME SERIES TIME SERIES CLUSTERING

Discovering patterns of online popularity from time series

10 Apr 2019mertozer/mts-clustering

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.

TIME SERIES TIME SERIES CLUSTERING

Learning Representations for Time Series Clustering

NeurIPS 2019 KMdsy/DTCR

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

3 Aug 2020EtienneGof/FunCLBM

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

18 Oct 2015avishaiwa/SPARCWave

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.

DIMENSIONALITY REDUCTION TIME SERIES TIME SERIES CLUSTERING