Work

Biclustering sustainable local tourism systems by the Tabu search optimization algorithm

Year: 2025

Type: article

Abstract: Tourism is nowadays fully acknowledged as a leading industry contributing to boost the economic development of a country. This growing recognition has led researchers and policy makers to increasingly focus their attention on all those concerns related to optimally detecting, promoting and supporting territorial areas with a high tourist vocation, i.e., Local Tourism Systems . In this work, we propose to apply the biclustering data mining technique to detect Local Tourism Systems. By means of a two-dimensional clustering approach, we pursue the objective of obtaining more in-depth and granular information than conventional clustering algorithms. To this end, we formulate the objective as an optimization problem, and we solve it by means of Tabu-search. The obtained results are very promising and outperform those provided by classic clustering approaches.

Source: Quality & Quantity

DOI: 10.1007/s11135-025-02105-x

Authors: Wassim Ayadi, Joseph Andria, Giacomo di Tollo, Gerarda Fattoruso

Institutions: Tunis University, Tunis El Manar University, University of Palermo, Marche Polytechnic University, University of Foggia

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,090