CLUSTERING PANEL DATA VIA PERTURBED ADAPTIVE SIMULATED ANNEALING AND GENETIC ALGORITHMS

Abstract

Non-hierarchical cluster analysis for panel data is known to be hampered by structural preservation, computational complexity and efficiency, and dependency problems. Resolving these issues becomes increasingly important as efficient collection and maintenance of panel data make application more conducive. To address some computational issues and structural preservation, Bonzo [3] presented a stochastic version of Kosmelj and Batagelj's approach [16] to clustering panel data. The method used a probability link function (instead of the usual distance functions) in defining cluster inertias with the aim of preserving the clusters' probabilistic structure. Formulating clustering as an optimization problem, the objective function allows the application of heuristic and stochastic optimization techniques. In this paper, we present a modified heuristic for adaptive simulated annealing (ASA) by perturbing the state vector's sampling distribution, specifically, by perturbing the drift of a diffusion process. Such an approach has been used to hasten convergence towards global optimum at equilibrium for diversely complex, combinatorial, and large-scale systems. The perturbed ASA (PASA) heuristic is then embedded in a genetic algorithm (GA) procedure to hasten and improve the stochastic local search process. The PASA-GA hybrid can be further modified and improved such as by explicit parallel implementation.