Investigating Multilevel Hesitated Patterns Using Vague Set Theory

Discovering hesitated frequent itemset is an important task in the online shopping scenario. Hesitated information is composed of those items that are hesitated during online purchasing by the customer and such items are termed as a hesitated itemset. Finding such type of information is crucial for business decision makers as through this knowledge, the state of the hesitated items can be converted to sold items by applying various promotions to the hesitated items. So, for finding frequently hesitated itemset an efficient algorithm is required. In this paper, the author aims in proposing an architecture of a hesitation mining system and a new algorithm for mining hesitated frequent itemset from the transactional database by using vague set theory and multilevel pattern mining.