English
Related papers

Related papers: Rank Intraclass Correlation for Clustered Data

200 papers

We present a technique for clustering categorical data by generating many dissimilarity matrices and averaging over them. We begin by demonstrating our technique on low dimensional categorical data and comparing it to several other…

Machine Learning · Statistics 2017-09-20 Saeid Amiri , Bertrand Clarke , Jennifer Clarke

Gathering training data is a key step of any supervised learning task, and it is both critical and expensive. Critical, because the quantity and quality of the training data has a high impact on the performance of the learned function.…

Data Structures and Algorithms · Computer Science 2021-10-28 Quentin Lutz , Élie de Panafieu , Alex Scott , Maya Stein

Clustering is a fundamental learning task widely used as a first step in data analysis. For example, biologists use cluster assignments to analyze genome sequences, medical records, or images. Since downstream analysis is typically…

Machine Learning · Computer Science 2024-06-11 Jonathan Svirsky , Ofir Lindenbaum

We introduce reputable citations (RC), a method to screen and segment a collection of papers by decoupling popularity and influence. We demonstrate RC using recent works published in a large set of mathematics journals from Clarivate's…

Digital Libraries · Computer Science 2025-03-05 Vicente Safón , Domingo Docampo , Lawrence Cram

Data clustering is an approach to seek for structure in sets of complex data, i.e., sets of "objects". The main objective is to identify groups of objects which are similar to each other, e.g., for classification. Here, an introduction to…

Data Analysis, Statistics and Probability · Physics 2016-02-17 Alexander K. Hartmann

In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours…

Physics and Society · Physics 2020-07-30 Roy Cerqueti , Gian Paolo Clemente , Rosanna Grassi

Invariant Coordinate Selection (ICS) is a multivariate technique that relies on the simultaneous diagonalization of two scatter matrices. It serves various purposes, including its use as a dimension reduction tool prior to clustering or…

Methodology · Statistics 2025-12-18 Colombe Becquart , Aurore Archimbaud , Anne Ruiz-Gazen , Luka Prilć , Klaus Nordhausen

Inference in clustering is paramount to uncovering inherent group structure in data. Clustering methods which assess statistical significance have recently drawn attention owing to their importance for the identification of patterns in high…

Methodology · Statistics 2021-06-18 Debora Zava Bello , Marcio Valk , Gabriela Bettella Cybis

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification,…

Machine Learning · Computer Science 2019-05-17 Patrick Ferber , Tengfei Ma , Siyu Huo , Jie Chen , Michael Katz

Inferring causal effects from an observational study is challenging because participants are not randomized to treatment. Observational studies in infectious disease research present the additional challenge that one participant's treatment…

Methodology · Statistics 2020-12-25 Brian G. Barkley , Michael G. Hudgens , John D. Clemens , Mohammad Ali , Michael E. Emch

Causal inference is difficult in the presence of unobserved confounders. We introduce the instrumented common confounding (ICC) approach to (nonparametrically) identify causal effects with instruments, which are exogenous only conditional…

Econometrics · Economics 2022-09-20 Christian Tien

We consider a clustering problem where we observe feature vectors $X_i \in R^p$, $i = 1, 2, \ldots, n$, from $K$ possible classes. The class labels are unknown and the main interest is to estimate them. We are primarily interested in the…

Methodology · Statistics 2015-12-17 Jiashun Jin , Wanjie Wang

Understanding the nature and organization of scientific communities is of broad interest. The `Invisible College' is a historical metaphor for one such type of community and the search for such `colleges' can be framed as the detection and…

Digital Libraries · Computer Science 2022-02-28 Shreya Chandrasekharan , Mariam Zaka , Stephen Gallo , Tandy Warnow , George Chacko

The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks. Such tasks include disease diagnosis, clinical trial recruitment, and continuing…

Signal Processing · Electrical Eng. & Systems 2021-10-05 Dani Kiyasseh , Tingting Zhu , David A. Clifton

We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion…

Machine Learning · Computer Science 2011-03-24 Kishor Barman , Onkar Dabeer

There is no, nor will there ever be, single best clustering algorithm. Nevertheless, we would still like to be able to distinguish between methods that work well on certain task types and those that systematically underperform. Clustering…

Machine Learning · Computer Science 2025-10-16 Marek Gagolewski

In this paper we propose a measure of clustering quality or accuracy that is appropriate in situations where it is desirable to evaluate a clustering algorithm by somehow comparing the clusters it produces with ``ground truth' consisting of…

Machine Learning · Computer Science 2013-01-07 Byron E Dom

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of…

Machine Learning · Computer Science 2018-05-16 David Hallac , Sagar Vare , Stephen Boyd , Jure Leskovec

Unsupervised image classification, or image clustering, aims to group unlabeled images into semantically meaningful categories. Early methods integrated representation learning and clustering within an iterative framework. However, the rise…

Computer Vision and Pattern Recognition · Computer Science 2025-11-21 Melih Baydar , Emre Akbas

Classification model selection is a process of identifying a suitable model class for a given classification task on a dataset. Traditionally, model selection is based on cross-validation, meta-learning, and user preferences, which are…

Machine Learning · Computer Science 2023-05-24 Sudarsun Santhiappan , Nitin Shravan , Balaraman Ravindran