An approach for predicting the missing items from large transaction database

The Internet is one of the fastest growing areas of intelligence gathering. Due to the tremendous amount of data on internet, web data mining has become very necessary. Predicting the missing items form dataset is indefinite area of research in Web Data Mining. Current approaches use association rule mining techniques which are applied to only small itemsets. Numbers of mechanisms were intended for “Frequent itemsets” but less attention has been paid that take the advantage of these frequent itemsets for prediction purpose. In order to reduce the rule mining cost for large dataset & to provide online prediction efficiently, the proposed approach use novel method for predicting the missing items. The proposed approach extends advantages of prediction at a higher level of abstraction and reduced rule generation complexity.