Knowledge discovery in databases (KDD) is to identify efficient and helpful information from large databases and provide automated analysis and solutions. In particular, finding association rules from transaction databases is most commonly seen in data mining. There are several algorithms have been developed to solve the problem to analysis the basket of the customer. These are mainly based on apriori. In fact mining pattern does not meet all the requirement of the business. Utility Mining is one of the extensions of Frequent Item set mining, which discovers item sets that occur frequently. In many real-life applications where utility item sets provide useful information in different decision-making domains such as business transactions, medical, security, fraudulent transactions, retail communities. Many algorithms have been developed to find high-utility patterns (HUPs) from databases without considering timestamp of patterns, especially in recent intervals. A pattern may not be a HUP in an entire database but may be a HUP in recent intervals. In this paper, an improved up-to-date high-utility pattern (UDHUP) is designed. It considers not only utility measure but also timestamp factor to discover the recent HUPs. In this paper the UDHUP- list algorithm is discussed. A new data structure, called up-to-date utility-list (UDU-list), is used to efficiently speed up the performance for mining UDHUPs.
展开▼
