Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Comparative statistical analyses of automated booleanization methods for data mining programs
Comparative statistical analyses of automated booleanization methods for data mining programs
Graph building as a mining activity: finding links in the small
Proceedings of the 3rd international workshop on Link discovery
Determining pattern element contribution in medical datasets
ACSW '07 Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68
Link analysis tools for intelligence and counterterrorism
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Analysis of a clinical head trauma dataset was aided by the use of a new, binary-based data mining technique, termed Boolean analyzer (BA), which finds dependency/association rules. With initial guidance from a domain user or domain expert, the BA algorithm is given one or more metrics to partition the entire dataset. The weighted rules are in the form of Boolean expressions. To augment the analysis of the rules produced, we applied a probabilistic interestingness measure (PIM) to order the generated rules based on event dependency, where events are combinations of primed and unprimed variables. Interpretation of the dependency rules generated on the clinical head trauma data resulted in a set of criteria that identified minor head trauma patients needing computed tomography (CT) scans. The BA criteria contained fewer variables than were found using recursive partitioning of Chi-square values (five variables versus seven variables, respectively). The BA five-variable criteria set was more sensitive but less specific than the seven-variable Chi-square criteria set. We believe that the BA method has broad applicability in the medical domain, and hope that this paper will stimulate other creative applications of the technique.