Mining high utility itemsets using TKO and TKU to find top-k high utility web access patterns

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant approaches have been proposed in recent years, but they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. An emerging topic in the field of data mining is utility mining which not only considers the frequency of the itemsets but also considers the utility associated with the itemsets. The main objective of High Utility Itemset Mining is to identify itemsets that have utility values above a given utility threshold. Thus Utility mining plays an important role in many real-time applications and is an important research topic in data mining system to find the itemsets with high profit. In this paper we present the implementation of first module where pre-processing of dataset is done to remove unpromising data from web usage and product base dataset by using TopKRules and also we are proposing a new framework for Top-k high utility web access patterns, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU and TKO are proposed for mining such itemsets. In this paper we present a literature review of the present state of research and the various algorithms for high utility itemset mining.