Post-Processing of Discovered Association Rules Using Ontologies

In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. In this paper we propose a new approach to prune and filter discovered rules. Using Domain Ontologies, we strengthen the integration of user knowledge in the post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task. On the one hand, we represent user domain knowledge using a Domain Ontology over database. On the other hand, a novel technique is suggested to prune and to filter discovered rules. The proposed framework was applied successfully over the client database provided by Nantes Habitat

Visualizing association rules in hierarchical groups

Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Although visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming, most association rule visualization techniques are still falling short when it comes to large numbers of rules. In this paper we introduce a new interactive visualization method, the grouped matrix representation, which allows to intuitively explore and interpret highly complex scenarios. We demonstrate how the method can be used to analyze large sets of association rules using the R software for statistical computing, and provide examples from the implementation in the R-package arulesViz. (authors' abstract

Administrative Sabotage

Government can sabotage itself. From the president’s choice of agency heads to agency budgets, regulations, and litigating positions, presidents and their appointees have undermined the very programs they administer. But why would an agency try to put itself out of business? And how can agencies that are subject to an array of political and legal checks sabotage statutory programs? This Article offers an account of the “what, why, and how” of administrative sabotage that answers those questions. It contends that sabotage reflects a distinct mode of agency action that is more permanent, more destructive, and more democratically illegitimate than more-studied forms of maladministration. In contrast to an agency that shirks its statutory duties or drifts away from Congress’s policy goals, one engaged in sabotage aims deliberately to kill or nullify a program it administers. Agencies sabotage because presidents ask them to. Facing pressure to dismantle statutory programs in an environment where securing legislation from Congress is difficult and politically costly, presidents pursue retrenchment through the administrative state. Building on this positive theory of administrative sabotage, this Article considers legal responses. The best response, this Article contends, is not reforms to the cross-cutting body of administrative law that structures most agency action. Rather, the risk of sabotage is better managed through changes to how statutory programs are designed. Congress’s choices about agency leadership, the concentration or dispersal of authority to implement statutory programs, the breadth of statutory delegations, and other matters influence the likelihood that sabotage will succeed or fail. When lawmakers create or modify federal programs, they should design them to be less vulnerable to sabotage by the very agencies that administer them

Interest Measures for Fuzzy Association Rules Based on Expectations of Independence

Lift, leverage, and conviction are three of the best commonly known interest measures for crisp association rules. All of them are based on a comparison of observed support and the support that is expected if the antecedent and consequent part of the rule were stochastically independent. The aim of this paper is to provide a correct definition of lift, leverage, and conviction measures for fuzzy association rules and to study some of their interesting mathematical properties

Knowledge-Based Systems. Overview and Selected Examples

The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology to create expert systems that have practical applications. By emphasizing a directly understandable problem representation, based on symbolic simulation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, models of complex processes are made understandable and available to non-technical users. Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way, e.g., "Expert Systems for Integrated Development: A Case Study of Shanxi Province, The People's Republic of China." This paper gives an overview of some of the expert systems that have been considered, compared or assessed during the course of our research, and a brief introduction to some of our related in-house research topics

Bringing Nordic mathematics education into the future : Preceedings of Norma 20 : The ninth Nordic conference on mathematics education Oslo, 2021

publishedVersio

Bringing Nordic mathematics education into the future : Preceedings of Norma 20 : The ninth Nordic conference on mathematics education Oslo, 2021

This volume presents Nordic mathematics education research, which will be presented at the Ninth Nordic Conference on Mathematics Education, NORMA 20, in Oslo, Norway, in June 2021. The theme of NORMA 20 regards what it takes or means to bring Nordic mathematics education into the future, highlighting that mathematics education is continuous and represents stability just as much as change.publishedVersio

Interest Measures for Fuzzy Association Rules Based on Expectations of Independence

Lift, leverage, and conviction are three of the best commonly known interest measures for crisp association rules. All of them are based on a comparison of observed support and the support that is expected if the antecedent and consequent part of the rule were stochastically independent. The aim of this paper is to provide a correct definition of lift, leverage, and conviction measures for fuzzy association rules and to study some of their interesting mathematical properties