Algorithms for approximate string matching
Information and Control
Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
Conceptual clustering in a first order logic representation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Clustering Algorithms
Intelligent Data Analysis in Medicine and Pharmacology
Intelligent Data Analysis in Medicine and Pharmacology
ECML '97 Proceedings of the 9th European Conference on Machine Learning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Distance Between Herbrand Interpretations: A Measure for Approximations to a Target Concept
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A Framework for Defining Distances Between First-Order Logic Objects
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Term Comparisons in First-Order Similarity Measures
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Relational Distance-Based Clustering
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Distances and Limits on Herbrand Interpretations
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Intelligent data analysis
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Cluster-based concept invention for statistical relational learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
Prototypes Based Relational Learning
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Description and classification of complex structured objects by applying similarity measures
International Journal of Approximate Reasoning
Combining Multiple Interrelated Streams for Incremental Clustering
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning with kernels and logical representations
Probabilistic inductive logic programming
Similarity-Based Classification in Relational Databases
Fundamenta Informaticae
From inductive logic programming to relational data mining
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Information Sciences: an International Journal
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Conceptual clustering of multi-relational data
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Exploiting domain knowledge to detect outliers
Data Mining and Knowledge Discovery
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Within data analysis, distance-based methods have always been very popular. Such methods assume that it is possible to compute for each pair of objects in a domain their mutual distance (or similarity). In a distance-based setting, many of the tasks usually considered in data mining can be carried out in a surprisingly simple yet powerful way. In this chapter, we give a tutorial introduction to the use of distance-based methods for relational representations, concentrating in particular on predictive learning and clustering. We describe in detail one relational distance measure that has proven very successful in applications, and introduce three systems that actually carry out relational distance-based learning and clustering: RIBL2, RDBC and FORC. We also present a detailed case study of how these three systems were applied to a domain from molecular biology.