Cluster Analysis of A Tobacco Control Data Set

Development of theoretical and methodological frameworks in data analysis is fundamental for modeling complex tobacco control systems. Following this idea, a new optimization based approach was introduced in the paper through two distinct methods: the modified linear least square fit and a heuristic algorithm for feature selection based on optimization techniques. Compared with traditional statistical techniques, optimization-based methods have the potential to detect nonlinearity, and therefore to be more effective analysis tools of complex data set. In this study we evaluate the modified global k-means clustering algorithm by applying it to a massive set of real-time tobacco control survey data. Cluster analysis identified fixed and stable clusters in the studied data. These clusters correspond to groups of smokers with similar behavior and the identification of these clusters may allow us to give recommendations on modification of existing tobacco control systems and on the design of future data acquisition surveys.