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.
Breathing k-means outperforms greedy k-means++ in clustering.
PNN-smoothing improves -means clustering by merging subsets' clusterings.
Proposes a new clustering method based on expectiles for non-spherical clusters.
Outlier detection is a technique in data mining that aims to detect unusual or unexpected records in the dataset. Existing outlier detection algorithms have different pros and cons and exhibit different sensitivity to noisy data such as extreme values. In this paper, we propose a novel cluster-based outlier detection a…
A new method detects concept drift in streaming data using k-means space partitioning.
Improved K-Means++ and K-Means with faster run-time.
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…
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…
Paper improves clustering risk bounds for kernel k-means.
Ball k-means reduces point-centroid distance computations for faster k-means clustering.
New method speeds up k-means clustering using sketch-and-solve.
A new k-means variant minimizes pairwise distances within clusters.
TS-K-means improves financial data clustering with dynamic time warping.
Paper proposes a novel unsupervised feature selection method using K-means and ADMM.
A faster Wasserstein k-means algorithm for histogram data reduces computation and maintains clustering quality.
A new -means method improves clustering accuracy and robustness.
We show that -means (Lloyd's algorithm) is obtained as a special case when truncated variational EM approximations are applied to Gaussian Mixture Models (GMM) with isotropic Gaussians. In contrast to the standard way to relate -means and GMMs, the provided derivation shows that it is not required to consider Gau…
Two algorithms improve K-means clustering speed without sacrificing quality.
We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential of deep k-means to outperform traditional two-step feature extraction and shallow-clustering strategies. We achieve this by developing a gradient-estimat…
The study investigates the consistency of -means clustering under finite expectation assumptions.
The paper assesses conditions for the uniqueness of k-means clustering.
Finding the optimal -means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the -means objective. We first show how to extend Lloyd's batched relocation heuristic and Hartigan's single-point relocation heuristic to take into account empty-cluster and single-poin…
Data-target association is an important step in multi-target localization for the intelligent operation of un- manned systems in numerous applications such as search and rescue, traffic management and surveillance. The objective of this paper is to present an innovative data association learning approach named multi-la…
K-Means and RBF networks are shown to be equivalent under certain conditions.
Study reduces dimensions for -means clustering for better accuracy.
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…
Transform learning improves K-means clustering for document analysis.
Fair k-means algorithm ensures equitable costs for different groups.
One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can obtain a suitable feature space. Nevertheless, while K-Means is one of the most ef…
A new Wasserstein -means method for clustering probability distributions.
K-means fails in high dimensions with noise and few samples.
K-means fails catastrophically in high dimensions, Hartigan's avoids it.
Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means an…
We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data w…
Two new scalable K-means initialization methods proposed for large-scale clustering.
Identifying a set of homogeneous clusters in a heterogeneous dataset is one of the most important classes of problems in statistical modeling. In the realm of unsupervised partitional clustering, k-means is a very important algorithm for this. In this technical report, we develop a new k-means variant called Augmented …
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,…
We study the problem of estimating a manifold from random samples. In particular, we consider piecewise constant and piecewise linear estimators induced by k-means and k-flats, and analyze their performance. We extend previous results for k-means in two separate directions. First, we provide new results for k-means rec…
This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data coul…
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…
Adapts manifold structure for better clustering performance.
Study explores K-means clustering of variables and its relation to PCA.
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 …
Kernel -means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear -means clustering algorithm. However, kernel -means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. Kernel …
Proposes a new K-means method for efficient clustering of nonlinear data.
We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets to estimate the subspaces. As the geometric structure of the clusters …