Data Preprocessing for Web Combinatorial Problems

Abstract

In the field of data science, we consider usually data independently from a problem to be solved. The originality of this paper consists in handling huge instances of combinatorial problems with datamining technologies in order to reduce the complexity of their treatment. Such task can be performed on Web combinatorial optimization such as internet data packet routing and web clustering. We focus in particular on the satisfiability of Boolean formulae but the proposed idea could be adopted for any other complex problem. The aim is to explore the satisfiability instance using datamining techniques in order to reduce its size, prior to solve it. An estimated solution for the obtained instance is then computed using a hybrid algorithm based on DPLL technique and a genetic algorithm. It is then compared to the solution of the initial instance in order to validate the method effectiveness. We performed experiments on the wellknown BMC datasets and show the benefits of using datamining techniques as a pretreatment, prior to solving the problem.