Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: concepts and techniques
Data mining: concepts and techniques
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Understanding Decision Support Systems and Expert Systems
Understanding Decision Support Systems and Expert Systems
Relational Data Mining
Distance based approaches to relational learning and clustering
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Data Mining and Knowledge Discovery
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Experiments in Predicting Biodegradability
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Genetic-Based Feature Construction Method for Data Summarisation
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Rules Extraction Based on Data Summarisation Approach Using DARA
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Dynamic Aggregation of Relational Attributes Based on Feature Construction
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
AlphaSum: size-constrained table summarization using value lattices
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Reducing metadata complexity for faster table summarization
Proceedings of the 13th International Conference on Extending Database Technology
Discretization numbers for multiple-instances problem in relational database
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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A new approach is needed to handle huge dataset stored in multiple tables in a very-large database. Data mining and Knowledge Discovery in Databases (KDD) promise to play a crucial role in the way people interact with databases, especially decision support databases where analysis and exploration operations are essential. In this paper, we present related works in Relational Data Mining, define the basic notions of data mining for decision support and the types of data aggregation as a means of categorizing or summarizing data. We then present a novel approach to relational domain learning to support the development of decision making models by introducing automated construction of hierarchical multi-attribute model for decision making. We will describe how relational dataset can naturally be handled to support the construction of hierarchical multi-attribute model by using relational aggregation based on pattern’s distance. In this paper, we presents the prototype of “Dynamic Aggregation of Relational Attributes” (hence called DARA) that is capable of supporting the construction of hierarchical multi-attribute model for decision making. We experimentally show these results in a multi-relational domain that shows higher percentage of correctly classified instances and illustrate set of rules extracted from the relational domains to support decision-making.