Sustainability and Biclustering: An applicative study

The ongoing increasing importance of the tourism industry along with its multidimensionality and complexity have attracted in recent years great attention from both academics and policymakers. A great concern in public policy has been given to the themes pertaining to local government systems in order to strengthen territorial cooperation and governance [1]. With this regard, particular attention is drawn to clustering approaches which stems from the acknowledgement of their positive influence on companies' performance, countries' competitiveness and regional development [2]. As also stated by [3], clusters facilitate innova6on by contributing to the development of innovative processes, promote and strengthen the networking with other institutions, they are also able to better intercept consumers' demand and interests and boost technology development by inducing the synthesis of knowledge and informa6on needs of all stakeholders. Some real-experienced clustering strategies in tourism aim to foster destination development through policy orientations, marketing programs and attracting investments. The objective of this work is to use a biclustering algorithm, which is a two-dimensional clustering technique, to find spatial neighbours among tourist destination areas, i.e., Local Tourism Systems. We want to obtain aggregates that are better (with respect to some sustainability criteria) to those attained by adopting a standard one-dimensional clustering approach. To this end, we formulate the aforementioned objective as an optimization problem, and we solve it by means of Tabu Search. This last is used to prevent the biclustering algorithm from re-evaluating solutions (biclusters) already processed and targeted as unpromising [4]. The obtained results seem very promising and outperform those reported in the literature [5,6] demonstra6ng the efficacy of our proposed algorithms.