Publisher's Synopsis
Business Analytics is the most important tool in today's marketplace. At present, almost every organization is using Business Analytics to improve their decision-making process. Since it offers better understanding of vast primary and secondary data, it can improve operational efficiency of the organizations. It uses several techniques such as quantitative and statistical analysis, pattern matching, data visualization, and predictive modeling to develop data-driven competitive strategies which can generate impressive business results. Various data mining techniques can be efficiently used in Business Analytics to perform various critical tasks (e.g. clean the data, deal with missing values, examine various relationships among attributes of the data, predict class of a given instances etc.). For instance, methods like classification algorithms can be used to predict stock prices, whether a customer will buy a term insurance, whether a borrower will repay his loan etc. Use of data mining techniques can improve performance of the decision support system of a business organization significantly. In this work, several important application areas haven been explored to find suitable data mining techniques. These application areas are: Business Forecasting, Recommendation System, Text Mining, Customer Segmentation, and Graph Mining. In the Business Forecasting domain, we have considered Movie Business. Combinations of various profitable movie genres have been considered as a trend. These Trends have been determined and then predicted. Rating and business of the movie have been predicted on the basis of reviews expressed on movie trailer as well as other attributes (e.g. popularity of actors/ actresses and movie makers, budget, time of release etc.). We have addressed the Query Recommendation problem in this work. Two Genetic Algorithm based solutions have been proposed here. In the text mining domain,
