reorderbench/ReorderBench_train_binary

1---2license: mit3---4# ReorderBench : A Benchmark for Matrix Reordering5Matrix reordering permutes the rows and columns of a matrix to reveal meaningful visual patterns, such as blocks that represent clusters. A comprehensive collection of matrices, along with a scoring method for measuring the quality of visual patterns in these matrices, contributes to building a benchmark. This benchmark is essential for selecting or designing suitable reordering algorithms for revealing specific patterns. In this paper, we build a matrix-reordering benchmark, ReorderBench, with the goal of evaluating and improving matrix-reordering techniques. This is achieved by generating a large set of representative and diverse matrices and scoring these matrices with a convolution- and entropy-based method. Our benchmark contains 2,835,000 binary matrices and 5,670,000 continuous matrices, each generated to exhibit one of four visual patterns: block, off-diagonal block, star, or band, along with 450 real-world matrices featuring hybrid visual patterns. We demonstrate the usefulness of ReorderBench through three main applications in matrix reordering: 1) evaluating different reordering algorithms, 2) creating a unified scoring model to measure the visual patterns in any matrix, and 3) developing a deep learning model for matrix reordering.6 7## What is ReorderBench?8ReorderBench is a large-scale matrix benchmark built for matrix reordering. ReorderBench has the following features:9- [x] Visual pattern recognition10- [x] 2,835,000 binary matrices, 5,670,000 continuous matrices, and 450 real-world matrices with expert-annotated visual patterns11- [x] 4 visual patterns: block, off-diagonal block, star, and band12- [x] 4 matrix sizes: 100x100, 200x200, 300x300, and 400x40013- [x] Scores to measure the quality of visual patterns14 15Here are the binary matrices of the ReorderBench training set.16 17You can generate the exact same data, as well as data for continuous matrices, via our generation script. For more information, please visit https://reorderbench.github.io/.