arXiv research

A new $k$-means method improves clustering accuracy and robustness.

Ball k-means reduces point-centroid distance computations for faster k-means clustering.

New method speeds up k-means clustering using sketch-and-solve.

The study investigates the consistency of $k$-means clustering under finite expectation assumptions.

Paper improves clustering risk bounds for kernel k-means.

Transform learning improves K-means clustering for document analysis.

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

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…

A new Wasserstein $K$-means method for clustering probability distributions.

A new k-means variant minimizes pairwise distances within clusters.

Study explores K-means clustering of variables and its relation to PCA.

Study reduces dimensions for $k$-means clustering for better accuracy.

Breathing k-means outperforms greedy k-means++ in clustering.

Kernel $k$-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear $k$-means clustering algorithm. However, kernel $k$-means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. Kernel …

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…

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 …

Selective inference controls Type I error in k-means clustering tests.

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…

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…

Fair k-means algorithm ensures equitable costs for different groups.

We introduce the {\it diffusion $K$-means} clustering method on Riemannian submanifolds, which maximizes the within-cluster connectedness based on the diffusion distance. The diffusion $K$-means constructs a random walk on the similarity graph with vertices as data points randomly sampled on the manifolds and edges as …

Proposes PKM for soft K-means clustering.

A novel clustering method using a subset of data for better performance.

Two algorithms improve K-means clustering speed without sacrificing quality.

Finding the optimal $k$-means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the $k$-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…

A novel k-means method for MNAR data improves clustering accuracy.

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, …

Adapts manifold structure for better clustering performance.

New insights into spurious local minima in k-means clustering.

Paper proposes a novel unsupervised feature selection method using K-means and ADMM.

The paper analyzes $k$-means clustering for missing data, proving statistical guarantees under MCAR.

A faster Wasserstein k-means algorithm for histogram data reduces computation and maintains clustering quality.

Robust Trimmed k-means improves clustering with outliers and mixed data.

Many clustering algorithms exist that estimate a cluster centroid, such as K-means, K-medoids or mean-shift, but no algorithm seems to exist that clusters data by returning exactly K meaningful modes. We propose a natural definition of a K-modes objective function by combining the notions of density and cluster assignm…

New $k$-means method clusters radar image sequences using SPD matrices.

A new k-means algorithm using cover trees accelerates clustering.

PNN-smoothing improves $k$-means clustering by merging subsets' clusterings.

Kernel clustering algorithm improved for large datasets using incomplete Cholesky factorization.

Paper presents a novel k-means clustering method using two distance measures for Gaussian data.

Two new scalable K-means initialization methods proposed for large-scale clustering.

Bayesian models offer great flexibility for clustering applications---Bayesian nonparametrics can be used for modeling infinite mixtures, and hierarchical Bayesian models can be utilized for sharing clusters across multiple data sets. For the most part, such flexibility is lacking in classical clustering methods such a…

We propose the Lasso Weighted $k$-means ($LW$-$k$-means) algorithm as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ($p$) can be much larger compared to the number of observations ($n$). In the $LW$-$k$-means algorithm, we introduce a lasso-based penalty term,…

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 …

EKM addresses imbalanced data clustering by repelling centroids in large clusters.

To cluster data that are not linearly separable in the original feature space, $k$-means clustering was extended to the kernel version. However, the performance of kernel $k$-means clustering largely depends on the choice of kernel function. To mitigate this problem, multiple kernel learning has been introduced into th…

A new fuzzy k-means algorithm for high-dimensional data with variable feature weights.

A fast algorithm for $K$-means clustering using subsampled SDP.

Generalizes k-means to graphs using PageRank.