Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Fundamentals of database systems
Fundamentals of database systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Category learning through multimodality sensing
Neural Computation
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Relational Data Mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ECML '93 Proceedings of the European Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Active learning with multiple views
Active learning with multiple views
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining relational databases with multi-view learning
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Pruning Relations for Substructure Discovery of Multi-relational Databases
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Multirelational classification: a multiple view approach
Knowledge and Information Systems
Privacy leakage in multi-relational learning via unwanted classification models
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
Reducing the size of databases for multirelational classification: a subgraph-based approach
Journal of Intelligent Information Systems
Iterative classification for multiple target attributes
Journal of Intelligent Information Systems
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Commercial relational databases currently store vast amounts of real-world data. The data within these relational repositories are represented by multiple relations, which are inter-connected by means of foreign key joins. The mining of such interrelated data poses a major challenge to the data mining community. Unfortunately, traditional data mining algorithms usually only explore one relation, the so-called target relation, thus excluding crucial knowledge embedded in the related so-called background relations. In this paper, we propose a novel approach for classifying relational such domains. This strategy employs multiple views to capture crucial information not only from the target relation, but also from related relations. This information is integrated into the relational mining process. The framework presented here, firstly, explore the relational domain to partition its features space into multiple subsets. Subsequently, these subsets are used to construct multiple uncorrelated views, based on a novel correlation-based view validation method, against the target concept. Finally, the knowledge possessed by multiple views are incorporated into a meta-learning mechanism to augment one another. Based on this framework, a wide range of conventional data mining methods can be applied to mine relational databases. Our experiments on benchmark real-world data sets show that the proposed method achieves promising results both in terms of overall accuracy obtained and run time, when compared with two other relational data mining approaches.