An Association Rule Mining Approach for Libraries to Analyse User Interest

In current scenario tremendous amount of transaction data is flooded in all domains of business processing where data mining is used as tool for predicting hidden knowledge from very large datasets. Data mining techniques plays an important role in business intelligence for analysing hidden facts in business processing. The objective of this work is to apply data mining techniques in academic libraries of institutions as they have huge volume of data with large hidden facts. Data mining technique association rule mining is applied to libraries to discover interesting facts from circulation data. The experimental results of association rule mining in academic libraries revealed interesting rules with respect to users interest in usage of books. The rules also help the institutions for arriving decision related to investment of budget for specific books titles.