arXiv research

Unsupervised clustering of series using dynamic programming.

DIVA clusters dynamic data without needing cluster count, outperforming baselines.

Improves scalability and robustness of dynamic graph clustering.

Integrable dynamics explained via geometric maps and cluster algebras.

Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size. We claim that it is possible to reuse information of past cluster assignments to expedite computation. Our approach builds on a recent idea of sidestepping the main bottleneck of spectral clusterin…

Bayesian method clusters time series with varying dynamics.

Linear dynamical systems are a fundamental and powerful parametric model class. However, identifying the parameters of a linear dynamical system is a venerable task, permitting provably efficient solutions only in special cases. This work shows that the eigenspectrum of unknown linear dynamics can be identified without…

A new clustering method for vector time series using autoregressive dynamics.

Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. Also there is often a gap between statistical guarantee and computational efficiency for existing tensor clust…

FOSC-X: An extended framework for extracting multiple optimal flat clusterings from hierarchical cluster trees

New method clusters evolving networks using spatio-temporal graph Laplacian.

In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static during the training process. The absence of concre…

We consider a context-based dynamic pricing problem of online products, which have low sales. Sales data from Alibaba, a major global online retailer, illustrate the prevalence of low-sale products. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples.…

DynMSA detects market clusters for better portfolio allocation.

A new method uses matrix sketches for efficient graph clustering in dynamic environments.

This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.

CW-EDMD improves prediction accuracy by learning local Koopman models for different state-space regions.

Study clusters bank customers using LSTM and DTW.

Interpolates mean shift and spectral clustering on graphs.

A Kyle-inspired model with adaptive agents explains excess volatility and volatility clustering.

An adaptive clustering algorithm learns from evolving data without manual tuning.

TS-K-means improves financial data clustering with dynamic time warping.

Proposes ICC method for dynamic portfolio optimization.

Model for optimal cybersecurity investment considering clustered cyberattacks.

Cluster GARCH model improves multivariate GARCH for high-dimensional asset returns.

Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differing length. We propose a modification to DTW that performs individual and independent pairwise alignment of feature trajectories. The modified technique, termed feature trajectory dynamic time warping (FTDTW), is a…

Market dynamic is quantified in terms of the entropy $S(τ,n)$ of the clusters formed by the intersections between the series of the prices $p_t$ and the moving average $\widetilde{p}_{t,n}$. The entropy $S(τ,n)$ is defined according to Shannon as $\sum P(τ,n)\log P(τ,n),$ with $P(τ,n)$ the probability for the cluster t…

A tutorial on dynamic Laplacian for time-evolving data clusters.

New method clusters directed graphs using Koopman operators.

We propose a new method for clustering multivariate time-series data based on Dynamic Linear Models. Whereas usual time-series clustering methods obtain static membership parameters, our proposal allows each time-series to dynamically change their cluster memberships over time. In this context, a mixture model is assum…

This study uses moving average cluster entropy to analyze financial market dynamics.

In the present paper, we studied a Dynamic Stochastic Block Model (DSBM) under the assumptions that the connection probabilities, as functions of time, are smooth and that at most $s$ nodes can switch their class memberships between two consecutive time points. We estimate the edge probability tensor by a kernel-type p…

A new MFG framework for evolving clusters from Gaussian mixtures.

We consider the $k$-means clustering problem in the dynamic streaming setting, where points from a discrete Euclidean space $\{1, 2, \ldots, Δ\}^d$ can be dynamically inserted to or deleted from the dataset. For this problem, we provide a one-pass coreset construction algorithm using space $\tilde{O}(k\cdot \mathrm{pol…

Selective reinitialization improves adaptability of neural bandits in dynamic environments.

Study clusters Kenyan medical insurance companies based on financial performance and reporting consistency.

DBS dynamically adjusts batch sizes to improve cluster utilization.

Particles representing tokens cluster in Transformers, influenced by initial tokens and matrix spectrum.

This note outlines a method for clustering time series based on a statistical model in which volatility shifts at unobserved change-points. The model accommodates some classical stylized features of returns and its relation to GARCH is discussed. Clustering is performed using a probability metric evaluated between post…

We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes from neuroscience, where an important problem is to identify, within a large assembly of neurons, subsets that respond similarly to a stimulu…

Study shows neural ODEs generalize well on synthetic graphs but struggle with degree heterogeneity and clustering.

We propose a combination of cluster analysis and stochastic process analysis to characterize high-dimensional complex dynamical systems by few dominating variables. As an example, stock market data are analyzed for which the dynamical stability as well as transitions between different stable states are found. This comb…

Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner. Such behavior can be well-captured by structured, nonparametric edge-exchangeable graphs. However, such exchangeable models necessarily ig…

Starting from a dataset with input/output time series generated by multiple deterministic linear dynamical systems, this paper tackles the problem of automatically clustering these time series. We propose an extension to the so-called Martin cepstral distance, that allows to efficiently cluster these time series, and a…

Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. Despite these advantages, convex clustering has not been widely adopted, due to its computationally intensive nature and its lack of compelling v…

DCMAP optimizes clustering in Bayesian Networks with dependent costs.

The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.

DeepDPM clusters images without knowing the number of clusters.