INTRODUCTION ASSOCIATION RULE DISCOVERY

T.S.Eliot, the famous poet, once said “Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?” . In every sphere of life, important decisions have to be taken in order to overcome the problems or to make profits in business. The information required for taking such decisions comes from the study of the data which is stored in large databases. Without rigorous analysis of these databases, it is not possible to extract important information underlying in that data. The sudden increase in the size of databases has resulted in the need for the development of such tools and techniques that are able to extract useful knowledge automatically by analyzing these large transactional databases in an efficient manner. Discovering the different patterns and behavior in these large databases is called knowledge discovery in database or data mining. Data Mining has been described as “"The non trivial extraction of implicit, previously unknown, and potentially useful information from data"”. It is used to find out some trends in real life which may not have been noticed but can be highly significant once they are discovered.

Most of the times the term “Data Mining” is confused with the term “Statistics” as both aims at exploring the structure of the data. While the groundwork was laid in the traditions of a statistician’s penchant for exhaustive collections of data, data mining techniques differ fundamentally from the conventional methods of analysis. Central to this difference is the weightage placed on the algorithm rather than the model. There are various data mining techniques used in the industry which can be broadly classified into classical techniques and next generation techniques. Some of them are clustering, decision trees, bayesian network, classification rules, data summarization, neural networks and many others. Association Rule Discovery is one such dimension of data mining which comes under the next generation techniques.