ABSTRACT
Recently, data can be collected and stored through various technological sources for different purposes in many field of study such as biology, medicine, geology, marketing and finance. Traditional methods that used for processing, analysing and modelling large databases are generally fail. In order to handle these steps, data mining methods are extremely preferred. One of the most important tasks in data mining is clustering. In general, clustering algorithms are partitioning a set of data objects into groups/subsets/clusters based on some similarity criterion. These algorithms aim to optimize a specific criterion such as minimizing the intra-cluster distances or maximizing the inter-cluster distances. For that reason, they can be considered as optimization problems. In this chapter, the operations research techniques including the use of mathematical programming for formulation of clustering problems are introduced. Reformulation of clustering problems as optimization problems are well-structured and, therefore, the solution of these problems presents competitive and promising results with the help of advanced of optimization techniques. Finally, several operations research applications that use clustering methods are provided in this chapter.
