Special Issue Article
Optimization Based Data Mining in Business Research
Business research is a process of collecting, classifying, analyzing and interpreting the data for making business decisions. Optimization in data mining and business research has found classical improvements in making decisions. Optimization in data mining provides a classical tool set to generate several data-driven classification systems, which helps in taking business decisions. Optimization approach in data mining strengthens the validity of organizing results and the improved collection, classification, analysis and interpretation of primary data. This paper focuses on the commonly used techniques for data mining viz., graph analysis, combinatorial optimization, Mathematical programming methods, Simulated and Genetic Method, Market Basket Analysis, APRIORI algorithms and randomized algorithms, Decision Tree induction method, K- means clustering algorithms, Drill down analysis. The optimization in data mining techniques helps decision makers to get precise and accurate information for making business decisions. Optimization techniques deal with data separation, classification. This paper also focuses on issues and Problems associated with data mining which involves optimal attribute subset, optimal number of clusters, missing values and incomplete data.