Akaike's information criterion and recent developments in information complexity
Journal of Mathematical Psychology
A robust and scalable clustering algorithm for mixed type attributes in large database environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Subgroup Analysis via Recursive Partitioning
The Journal of Machine Learning Research
A new data mining approach to estimate causal effects of policy interventions
Expert Systems with Applications: An International Journal
A multivariate strategy to measure and test global imbalance in observational studies
Expert Systems with Applications: An International Journal
Factorial k-means analysis for two-way data
Computational Statistics & Data Analysis
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Subgroup Analysis (SA) is a helpful technique in the context of randomised experiments and in observational studies. With reference to program evaluation, it helps in determining whether and how treatment effects vary across subgroups induced by baseline covariates. However, the choice of the optimal number of subgroups is often ambiguous and causes concern. Here, SA is conducted using the cluster-based approach introduced in D'Attoma and Camillo (2011) and the usage of the Information Complexity Criterion to select the optimal number of groups is proposed. A simulation study and a real case have been illustrated to show such promising approach.