Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
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Product Bundling and offering products to customers is of critical importance in retail marketing. In this paper, a predictive mining approach is presented that predicts sales for a new location based on the existing data. The major issue lies in the analysis of sales forecast based on the dependencies among the products and the different segment of customers, which helps to improve the market of the retail stores. The work presents a framework, which models an association relation mapping between the customers and the clusters of products they purchase in an existing location and helps in finding rules for an entirely new location. A novel methodology and model are proposed for accomplishing the task efficiently. The methodology is based on the integration of the popular data mining approaches such as clustering and association rule mining. It focuses on the discovery of rules that vary according to the economic and demographic characteristics and concentrates on marketing the products based on the population.