Protein Structure and Synthetic Multi-view Clustering Datasets

Protein Structure and Synthetic Multi-view Clustering DatasetsMulti-View Clustering (MVC) datasets used in the following paper:Evolutionary Multi-objective Clustering Over Multiple Conflicting Data Views Mario Garza-Fabre, Julia Handl, and Adán José-García IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION Accepted for publication in November 2022This entry contains all 420 datasets used in the paper, including:400 datasets of protein structures (Bioinformatics application). For these datasets, multiple dissimilarity matrices are provided, which can be exploited as data views during the clustering process. These matrices result from the use of distinct measures of structural similarity between the candidate structures. The process that we followed to construct these datasets is described in detail in our paper and its supplementary material.20 synthetic, two-dimensional problems. These problems present varying characteristics regarding the shape, overlap, and separability of the clusters. Multiple views are defined by the use of two different distance measures: Euclidean Distance (data view 1) and Maximum Edge Distance (data view 2).All data views (dissimilarity matrices) are provided as text files (*.txt), and additional files are included with detailed explanations regarding their organization and correct interpretation (README.txt, Problem_Information.csv, Problem_Information.xlsx).Contact: Mario Garza-Fabre ([e-mail]; [e-mail])