A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
TS-K-means improves financial data clustering with dynamic time warping.
sWk-means clusters multidimensional financial time series into distinct market regimes.
New -means method clusters radar image sequences using SPD matrices.
Method selects the best deep learner for time-series prediction using Bayesian networks.
For time series comparisons, it has often been observed that z-score normalized Euclidean distances far outperform the unnormalized variant. In this paper we show that a z-score normalized, squared Euclidean Distance is, in fact, equal to a distance based on Pearson Correlation. This has profound impact on many distanc…
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The…
K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from a series of disadvantages; it is only able to find local minima and the positions of the initial clustering centres (centroids) can greatly …
We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital market crash at the end of 2017 - beginning 2018. We introduce a methodology that combines topological data analysis with a machine learning technique -- -means clustering -- in order to automatical…
Improved K-Means++ and K-Means with faster run-time.
Study clusters Indian stocks using polyspectral means for nuanced market insights.
Paper proposes a new algorithm for clustering financial market regimes.
Update rules for learning in dynamic time warping spaces are based on optimal warping paths between parameter and input time series. In general, optimal warping paths are not unique resulting in adverse effects in theory and practice. Under the assumption of squared error local costs, we show that no two warping paths …
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…
K-ARMA models cluster time series data robustly.
Method detects lead-lag relationships in multivariate time series.
Over the last few years, traffic data has been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges traditional analytic methods. This paper proposes a new Divide and Combine based approach to do K means clust…
Neural clustering learns time series affinity from statistical features.
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for c…
Traditionally, practitioners initialize the {\tt k-means} algorithm with centers chosen uniformly at random. Randomized initialization with uneven weights ({\tt k-means++}) has recently been used to improve the performance over this strategy in cost and run-time. We consider the k-means problem with semi-supervised inf…
Efficient algorithms for -means clustering frequently converge to suboptimal partitions, and given a partition, it is difficult to detect -means optimality. In this paper, we develop an a posteriori certifier of approximate optimality for -means clustering. The certifier is a sub-linear Monte Carlo algorithm b…
Fair k-means algorithm ensures equitable costs for different groups.
New method preserves distances in time series data.
Efficient clustering for large datasets using a sampling-based approach.
We propose the Lasso Weighted -means (--means) algorithm as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features () can be much larger compared to the number of observations (). In the --means algorithm, we introduce a lasso-based penalty term,…
Fuzzy clustering with similarity queries improves efficiency and accuracy.
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. We extend the sparse K-means algorithm by incorporating structured sparsity, and use it to exploi…
Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-seri…
Deep network clusters hospital patients' vital signs.
In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to pe…
Generative Adversarial Networks (GANs) have achieved great success in generating realistic images. Most of these are conditional models, although acquisition of class labels is expensive and time-consuming in practice. To reduce the dependence on labeled data, we propose an un-conditional generative adversarial model, …
Combines k-means and hill climbing for stratification and allocation.
We introduce the {\it diffusion -means} clustering method on Riemannian submanifolds, which maximizes the within-cluster connectedness based on the diffusion distance. The diffusion -means constructs a random walk on the similarity graph with vertices as data points randomly sampled on the manifolds and edges as …
Breathing k-means outperforms greedy k-means++ in clustering.
We introduce an evolutionary algorithm called recombinator--means for optimizing the highly non-convex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the -means++ seeding algorithm. Th…
Paper solves globally optimal k-means for low dimensional data.
The present work proposes hybridization of Expectation-Maximization (EM) and K-Means techniques as an attempt to speed-up the clustering process. Though both K-Means and EM techniques look into different areas, K-means can be viewed as an approximate way to obtain maximum likelihood estimates for the means. Along with …
Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, …
A fast algorithm for -means clustering using subsampled SDP.
We propose a novel accelerated exact k-means algorithm, which performs better than the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3 times faster. We also propose a general improvement of existing state-of-the-art accelerated exact k-means algorithms through better estimate…
The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initial…
EKM addresses imbalanced data clustering by repelling centroids in large clusters.
A new restart criterion for k-means++ improves clustering quality and adapts to data difficulty.
A new -means method HT -means uses penalty for sparsity.
Modified K-means ensures local optimality with same complexity.
-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing. However, due to the thin-tailed property of the Gaussian distribution, -means algorithm suffers from relatively poor performance on the dataset containing heavy-tailed data or outlie…
Paper improves clustering risk bounds for kernel k-means.
Ball k-means reduces point-centroid distance computations for faster k-means clustering.
The elbow method for choosing k in k-means is unreliable and should be avoided.