Abstract:
High-utility itemset mining has attracted signicant attention from the
research community. Identifying high-utility itemsets from a transaction
database can help business owners to earn better profit by promoting the
sales of high-utility itemsets. The technique also finds applications in web-
click stream analysis, biomedical data analysis, mobile E-commerce etc.
Several algorithms have been proposed to mine high-utility itemsets from a
transaction database. However, these algorithms assume that items have a
constant profit associated with them and don't embed the notion of discount
into the utility-mining framework. In this thesis, we integrate the notion of
discount in state-of-the-art utility-mining algorithms and propose a hybrid-
algorithm for efficient mining of high-utility itemsets. We conduct extensive
experiments on real and synthetic datasets and our results show that our
proposed algorithm outperforms the state-of-the-art algorithms in terms of
total execution time and number of itemsets that need to be explored.
