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
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
TAR: Temporal Association Rules on Evolving Numerical Attributes
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
IEEE Transactions on Knowledge and Data Engineering
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
PRIM versus CART in subgroup discovery: When patience is harmful
Journal of Biomedical Informatics
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With the increased acceptance of electronic health records, we can observe the increasing interest in the application of data mining approaches within this field. This study introduces a novel approach for exploring and comparing temporal trends within different in-patient subgroups, which is based on associated rule mining using Apriori algorithm and linear model-based recursive partitioning. The Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality was used to evaluate the proposed approach. This study presents a novel approach where visual analytics on big data is used for trend discovery in form of a regression tree with scatter plots in the leaves of the tree. The trend lines are used for directly comparing linear trends within a specified time frame. Our results demonstrate the existence of opposite trends in relation to age and sex based subgroups that would be impossible to discover using traditional trend-tracking techniques. Such an approach can be employed regarding decision support applications for policy makers when organizing campaigns or by hospital management for observing trends that cannot be directly discovered using traditional analytical techniques.