Work

Fuzzy clustering of mixed data with spatial regularization

Year: 2024

Type: article

Abstract: A fuzzy clustering model for data with mixed features and spatial constraints is proposed. The clustering model allows different types of variables, or attributes, to be taken into account. This result is achieved by combining the dissimilarity measures for each attribute employing a weighting scheme, to obtain a distance measure for multiple attributes. The weights are objectively computed during the optimization process. The weights reflect the relevance of each attribute type in the clustering results. A spatial term is taken into account, considering a wide definition of contiguity, either physical contiguity or the adjacency matrix in a network. Simulation studies and two empirical applications, including both physical and abstract definitions of contiguity are presented that show the effectiveness of the proposed clustering model.

Source: Spatial Statistics

DOI: 10.1016/j.spasta.2024.100874

Authors: Pierpaolo D’Urso, Livia De Giovanni, Lorenzo Federico, Vincenzina Vitale

Institutions: Sapienza University of Rome, Libera Università Internazionale degli Studi Sociali Guido Carli

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,430

OpenAlex ID: W4404630429

FWCI: 0.59

Cites: 39

Cited by: 4

Related to: 10