Abstract: Data mining is the process of extracting interesting, useful and previously unknown information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. There are various types of data mining techniques such as association rules, classifications and clustering. Association rule mining is one of the most important and well researched techniques of data mining. Among sets of items in the transaction databases or other data repositories, it seeks interesting correlations, frequent patterns, associations or casual structures. Association Rule Mining is a very potential technique which has the aim to find interesting and useful patterns from the transactional database. It is mainly used in market basket analysis that help to identify patterns of all those items that are purchased together. To denote association with itemsets and their quantities, the Quantitative association mining is used. In this, we partition each item into equi-spaced bins with each bin representing a quantity range. It assumes each bin as a separate bin as we proceed with mining and we also take care to reduce redundancies and rules between different bins of the same item. Here, we make use of Association Rule Mining Technique to create a platform which helps in grouping similar objects together in a transaction process.