Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Learning Nonrecursive Definitions of Relations with LINUS
EWSL '91 Proceedings of the European Working Session on Machine Learning
Selective Propositionalization for Relational Learning
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Experiments in Meta-level Learning with ILP
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Multi-Relational Data Mining, Using UML for ILP
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Stochastic Propositionalization of Non-determinate Background Knowledge
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Involving Aggregate Functions in Multi-relational Search
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pruning Social Networks Using Structural Properties and Descriptive Attributes
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining
ADMA '07 Proceedings of the 3rd 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
Combining Multiple Interrelated Streams for Incremental Clustering
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
A Randomized Exhaustive Propositionalization Approach for Molecule Classification
INFORMS Journal on Computing
Label-dependent node classification in the network
Neurocomputing
Good and bad practices in propositionalisation
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Mining relational association rules for propositional classification
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Refining aggregate conditions in relational learning
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Mining model trees from spatial data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Data mining in inductive databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
MapReduce approach to collective classification for networks
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Active learning and inference method for within network classification
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Type Extension Trees for feature construction and learning in relational domains
Artificial Intelligence
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The fact that data is scattered over many tables causes many problems in the practice of data mining. To deal with this problem, one either constructs a single table by hand, or one uses a Multi-Relational Data Mining algorithm. In this paper, we propose a different approach in which the single table is constructed automatically using aggregate functions, which repeatedly summarise information from different tables over associations in the datamodel. Following the construction of the single table, we apply traditional data mining algorithms. Next to an in-depth discussion of our approach, the paper presents results of experiments on three well-known data sets.