High utility pattern mining becomes a very important research issue in data mining by considering the non-binary frequency values of items in transactions and different profit values for each item. These profit values can be computed efficiently inorder to determine the gain of an itemset which in-turn will help in production planning of any company. This gain value is needed to prune some of t...

Tzung-Pei Hong , Cho-Han Lee and Shyue-Liang Wang Department of Computer Science and Information Engineering Department of Electrical Engineering Department of Information Management National University of Kaohsiung, Kaohsiung, 811, Taiwan Department of Computer Science and Engineering National Sun Yat-sen University, Kaohsiung, 804, Taiwan [email protected], [email protected], slwang@nu...

High-utility itemset (HUI) mining is a popular data mining task, consisting of enumerating all groups of items that yield a high profit in a customer transaction database. However, an important issue with traditional HUI mining algorithms is that they tend to find itemsets having many items. But those itemsets are often rare, and thus may be less interesting than smaller itemsets for users. In ...

The Institute Council wishes it to be understood that opinions put forward herein are not necessarily those of the Institute and the Council is not responsible for those opinions. Abstract. The paper presents the findings of an industry-based study in the utility of text mining. The purpose of the study was to evaluate the impact of textual information in claims cost prediction. The industrial ...

High-utility itemset mining is the task of discovering highutility itemsets, i.e. sets of items that yield a high profit in a customer transaction database. High-utility itemsets are useful, as they provide information about profitable sets of items bought by customers to retail store managers, which can then use this information to take strategic marketing decisions. An inherent limitation of ...

One of the important issues in data mining is the interestingness problem. Typically, in a data mining process, the number of patterns discovered can easily exceed the capabilities of a human user to identify interesting results. To address this problem, utility measures have been used to reduce the patterns prior to presenting them to the user. A frequent itemset only reflects the statistical ...

Local Process Models (LPMs) describe structured fragments of process behavior occurring in the context of less structured business processes. In contrast to traditional support-based LPM discovery, which aims to generate a collection of process models that describe highly frequent behavior, High-Utility Local Process Model (HU-LPM) discovery aims to generate a collection of process models that ...

Conventional Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. Rare objects are often of great interest and great value. Until recently, rarity has not received much attention in the context of data mining. For many real world applications, however, utility of rare itemsets based on ...