Abstract Association rule mining is a technique that widely used in data mining. This to identify interesting relationships between sets of items dataset and predict associative behavior for new data. Before the formed, it must be determined advance which will involved or called frequent itemset. In this step, threshold eliminate excluded itemset also known as minimum support. Furthermore, prov...

Received Jan 14, 2017 Revised Jun 7, 2017 Accepted Sep 11, 2017 Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a w...

Mining frequently appearing patterns in a database is a basic problem in recent informatics, especially in data mining. Particularly, when the input database is a collection of subsets of an itemset, called transaction, the problem is called the frequent itemset mining problem, and it has been extensively studied. The items in a frequent itemset appear in many records simultaneously, thus they ...

Utility mining is a new expansion of data mining expertise. Among utility mining difficulties, utility mining with the itemset share framework is a solid one as no anti-monotonicity property grasps with the interestingness amount. Preceding works on this problem all service a two-phase, candidate generation method with one exemption that is however incompetent and not mountable with large datab...

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 ...

Significant efforts have been expended in the research and development of a database management system (DBMS) that has wide range applications for managing an enormous collection multisource, heterogeneous, complex, or growing data. Besides primary function (i.e., create, delete, update), practical impeccable DBMS can interact with users through information selection, is, querying their targets...

GUO-CHENG LAN, TZUNG-PEI HONG AND VINCENT S. TSENG Department of Computer Science and Information Engineering Institute of Medical Informatics National Cheng Kung University Tainan, 701 Taiwan E-mail: [email protected]; [email protected] Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung, 811 Taiwan Department of Computer Science and E...

Existing top-k high utility itemset (HUI) mining algorithms generate candidate itemsets in the mining process; their time & space performance might be severely affected when the dataset is large or contains many long transactions; and when applied to data streams, the performance of corresponding mining algorithm is especially crucial. To address this issue, propose a sliding window based top-k...

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 ...