Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
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
Principles of data mining
Distance based approaches to relational learning and clustering
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Using Logical Decision Trees for Clustering
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Distance Induction in First Order Logic
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
Which Hypotheses Can Be Found with Inverse Entailment?
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Visual cluster validity for prototype generator clustering models
Pattern Recognition Letters
Kernels and Distances for Structured Data
Machine Learning
Cross-relational clustering with user's guidance
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Collective entity resolution in relational data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
The Journal of Machine Learning Research
k-RNN: k-relational nearest neighbour algorithm
Proceedings of the 2008 ACM symposium on Applied computing
Extracting Semantic Networks from Text Via Relational Clustering
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Clustering relational data based on randomized propositionalization
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy
IEEE Transactions on Pattern Analysis and Machine Intelligence
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
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"Traditional" clustering, in broad sense, aims at organizing objects into groups (clusters) whose members are "similar" among them and are "dissimilar" to objects belonging to the other groups. In contrast, in conceptual clustering the underlying structure of the data together with the description language which is available to the learner is what drives cluster formation, thus providing intelligible descriptions of the clusters, facilitating their interpretation. We present a novel conceptual clustering system for multi-relational data, based on the popular k−medoids algorithm. Although clustering is, generally, not straightforward to evaluate, experimental results on several applications show promising results. Clusters generated without class information agree very well with the true class labels of cluster's members. Moreover, it was possible to obtain intelligible and meaningful descriptions of the clusters.