arXiv research

The paper generalizes Thurston's earthquake map to cluster algebras of finite type.

Proves log-concavity of cluster algebra coefficients for type $A_n$.

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

New system constructs cell-type taxonomy across multiple samples.

Forest Fire Clustering discovers cell types from single-cell data.

We give a method to compute presentations of saturated cluster modular groups. Using this, we obtain finite presentations of the saturated cluster modular groups of finite mutation type $X_6$ and $X_7$. We verify that the cluster modular groups of finite mutation type $\widetilde{E}_6$, $\widetilde{E}_7$, $\widetilde{E…

Quantum cluster algebras for surfaces with coefficients defined using skein theory.

A new method clusters mixed-type data efficiently.

Convex clustering can only learn convex clusters, with significant gaps between clusters.

We prove the existence of Lagrangian fillings for $D_n$-type Legendrian links.

This study evaluates cluster search algorithms using Gaussian mixture models.

New method interprets deep embeddings for diabetes patient clustering.

In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor pe…

Proposes selective inference for testing differences in means between clusters.

Study compares clustering methods for mixed-type data.

New model clusters cells and individuals, revealing genetic influences on cell types.

New model clusters mixed-type data with missing values, improving air quality analysis.

Fixed points found in cluster modular groups under specific conditions.

Elastic co-clustering improves clustering of single-cell genomic data.

Clustering is an essential technique for discovering patterns in data. The steady increase in amount and complexity of data over the years led to improvements and development of new clustering algorithms. However, algorithms that can cluster data with mixed variable types (continuous and categorical) remain limited, de…

Driven by the multi-level structure of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a new variant of a hierarchical Dirichlet Process---the multi-level clustering hierarchical Dirichlet Process (MLC-HDP)---that simultaneously clusters datasets on multiple levels. Our sei…

Proposes a method to cluster multi-aspect data using manifold learning with NMF.

This study reviews and evaluates clustering methods for single-cell RNA-seq data.

There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. Our focus falls on INteger-valued AutoRegressive (INAR) type models. The INAR type models can be used in conjunction with existing model-based clustering techniques to …

Advances neural tri-factorization for clustering and discordance analysis of multi-typed data.

Modern graph or network datasets often contain rich structure that goes beyond simple pairwise connections between nodes. This calls for complex representations that can capture, for instance, edges of different types as well as so-called "higher-order interactions" that involve more than two nodes at a time. However, …

Spectral clustering adapted for mixed data types without preprocessing.

A new method clusters mixed-type data tables effectively.

JojoSCL improves scRNA-seq clustering by reducing intra-cluster dispersion.

The paper clusters hypergraphs to find diverse and experienced groups based on past experiences.

Investigates webs related to cluster algebras and polylogarithms.

We establish basic properties of cluster algebras associated with oriented bordered surfaces with marked points. In particular, we show that the underlying cluster complex of such a cluster algebra does not depend on the choice of coefficients, describe this complex explicitly in terms of "tagged triangulations" of the…

In a recent work, [19] studied the following "fair" variants of classical clustering problems such as $k$-means and $k$-median: given a set of $n$ data points in $\mathbb{R}^d$ and a binary type associated to each data point, the goal is to cluster the points while ensuring that the proportion of each type in each clus…

IMPACC improves consensus clustering for bioinformatics data.

We present a new model-based integrative method for clustering objects given both vectorial data, which describes the feature of each object, and network data, which indicates the similarity of connected objects. The proposed general model is able to cluster the two types of data simultaneously within one integrative p…

We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one first has to construct a graph on the data points and then apply a graph clustering algorithm to find a suitable partition of the graph. Our main question is if and how the construction of the graph (…

MMM model clusters mixed-type longitudinal data efficiently.

In this paper, we study the distribution of the genuses of cluster quivers of finite mutation type. First, we prove that in the $11$ exceptional cases, the distribution of genuses is $0$ or $1$. Next, we consider the relationship between the genus of an oriented surface and that of cluster quivers from this surface. It…

We present an asymptotic criterion to determine the optimal number of clusters in k-means. We consider k-means as data compression, and propose to adopt the number of clusters that minimizes the estimated description length after compression. Here we report two types of compression ratio based on two ways to quantify t…

End-to-end deep learning for multi-view clustering improves accuracy across various data types.

It has been noticed that some external CVIs exhibit a preferential bias towards a larger or smaller number of clusters which is monotonic (directly or inversely) in the number of clusters in candidate partitions. This type of bias is caused by the functional form of the CVI model. For example, the popular Rand index (R…

Clustering is one of the most fundamental and wide-spread techniques in exploratory data analysis. Yet, the basic approach to clustering has not really changed: a practitioner hand-picks a task-specific clustering loss to optimize and fit the given data to reveal the underlying cluster structure. Some types of losses--…

Proposes a new model for clustering multiplex networks with compositional data.

Let $S$ be a surface, $G$ a simply-connected classical group, and $G'$ the associated adjoint form of the group. In \cite{FG1}, it was shown that the moduli spaces of framed local systems $\X_{G',S}$ and $\A_{G,S}$ have the structure of cluster varieties, and thus together form a cluster ensemble, when $G$ had type $A$…

Modeling price clustering in financial markets using discrete distributions.

Study proposes a statistical testing framework for evaluating clustering pipelines.

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving both the robustness and the stability of results from individual clustering methods. Weighted clustering ensemble arises naturally from clustering ensemble. One of the arguments for weighted clustering ensemble is that elements (…

scICML integrates multi-omics data from single cells using co-clustering.