Quantitative Association Rules Mining for Business Transactional Data

Abstract

The explosive growth in data and database has generated a need for techniques and tools that can transform the processed data into useful information and knowledge that improves marketing strategy. Association rules mining is finding frequent patterns, associations, correlations, or causal structures among item sets in transaction databases, relational databases, and other information repositories. The relational tables that stored the transactions have richer attribute types such as quantitative and categorical attribute. Thus the development of tools that can extract useful information from this large database is greatly demand. This paper discusses the quantitative association rules mining from business transactional database that store the textile store. We introduce the quantitative association rules mining using with the direct application using on a real-life dataset.