Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Visualizing association rules with interactive mosaic plots
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing data mining models
Information visualization in data mining and knowledge discovery
Dynamic Queries for Visual Information Seeking
IEEE Software
DBMiner: a system for data mining in relational databases and data warehouses
CASCON '97 Proceedings of the 1997 conference of the Centre for Advanced Studies on Collaborative research
The Semiotic Engineering of Human-Computer Interaction (Acting with Technology)
The Semiotic Engineering of Human-Computer Interaction (Acting with Technology)
Anteater: A Service-Oriented Architecture for High-Performance Data Mining
IEEE Internet Computing
Data Mining: um Guia Prático
Visualization of directed associations in e-commerce transaction data
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
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Data mining focuses on extracting useful information from great volumes of data, and thus has been the center of great attention in the recent years. Among the many techniques available for data mining, identifying association rules is one of the most popular. The novel aspect of rule association mining systems brings new challenges to the HCI field. In this article, we identify these challenges and analyze them based on the theory of action, and characterize it within the semiotic engineering theoretical framework. Thus, we provide designers with an explanation of aspects to be considered during use and design of such systems. This theoretical based explanation contributes to a deeper understanding of the issues involved in interacting with association rules mining systems, allowing for better informed decisions during design process. It also motivates future empirical investigations.