Level Clustering

Level clustering is a technique that organizes data into hierarchical structures, grouping similar items into clusters at multiple levels of granularity. Current research focuses on developing efficient algorithms, such as those employing graph convolutional networks and Wasserstein distances, to perform this clustering across diverse data types, including time series, graphs, and multi-view data. These advancements improve the accuracy and speed of tasks like forecasting, graph classification, and document layout analysis, demonstrating the practical impact of level clustering across various fields. The unified treatment of multi-level clustering is a significant trend, improving both efficiency and performance compared to single-level approaches.

Papers

July 1, 2024

Multi-level Reliable Guidance for Unpaired Multi-view Clustering
Like Xin, Wanqi Yang, Lei Wang, Ming Yang
Cross View Multi View Clustering Robust Guidance Incomplete Multi View Clustering Level Clustering

September 2, 2022

Higher-order Clustering and Pooling for Graph Neural Networks
Alexandre Duval, Fragkiskos Malliaros
Graph Neural Network Graph Representation Graph Classification Node Embeddings Graph Pooling Level Clustering

May 27, 2022

Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
Xing Han, Tongzheng Ren, Jing Hu, Joydeep Ghosh, Nhat Ho
Time Series Data Hierarchical Time Series Efficient Time Series Forecasting Global Clustering Level Clustering

March 17, 2022

Unified Line and Paragraph Detection by Graph Convolutional Networks
Shuang Liu, Renshen Wang, Michalis Raptis, Yasuhisa Fujii
Deep Convolutional Neural Network Unified Alignment Text Detection Paragraph Speech Paragraph Level Tree Structure Level Clustering

Level Clustering

Papers

Multi-level Reliable Guidance for Unpaired Multi-view Clustering

Higher-order Clustering and Pooling for Graph Neural Networks

Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering

Unified Line and Paragraph Detection by Graph Convolutional Networks