Weighted Association Rule Mining for Item Groups with Different Properties and Risk Assessment for Networked Systems

• Published in: IEICE Transactions on Information and Systems Vol. E92.D; no. 1; pp. 10 - 15
• Main Authors: KIM, Jungja, CEONG, Heetaek, WON, Yonggwan
• Format: Journal Article
• Language: English
• Published: Oxford The Institute of Electronics, Information and Communication Engineers 01.01.2009; Oxford University Press
• Subjects: Applied sciences; Computer science; control theory; systems; Computer systems; Computer systems and distributed systems. User interface; different item groups; Electronics; Exact sciences and technology; Hardware; Information systems. Data bases; Memory organisation. Data processing; networked systems; risk assessment; Software; Telecommunications; Telecommunications and information theory; Teleprocessing networks. Isdn; Valuation and optimization of characteristics. Simulation; weighted association rule; Vulnerability; Market survey; Risk analysis; Network service; networked systems; Algorithm; Networked system; different item groups; Computer network; Risk assessment; Mine; Database; Computer system; Safety; Telecommunication services; weighted association rule
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.E92.D.10

In market-basket analysis, weighted association rule (WAR) discovery can mine the rules that include more beneficial information by reflecting item importance for special products. In the point-of-sale database, each transaction is composed of items with similar properties, and item weights are pre-defined and fixed by a factor such as the profit. However, when items are divided into more than one group and the item importance must be measured independently for each group, traditional weighted association rule discovery cannot be used. To solve this problem, we propose a new weighted association rule mining methodology. The items should be first divided into subgroups according to their properties, and the item importance, i.e. item weight, is defined or calculated only with the items included in the subgroup. Then, transaction weight is measured by appropriately summing the item weights from each subgroup, and the weighted support is computed as the fraction of the transaction weights that contains the candidate items relative to the weight of all transactions. As an example, our proposed methodology is applied to assess the vulnerability to threats of computer systems that provide networked services. Our algorithm provides both quantitative risk-level values and qualitative risk rules for the security assessment of networked computer systems using WAR discovery. Also, it can be widely used for new applications with many data sets in which the data items are distinctly separated.