Conceptual clustering in a first order logic representation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
ACM Computing Surveys (CSUR)
Generality-Based Conceptual Clustering with Probabilistic Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Logical Decision Trees for Clustering
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Top-down induction of first-order logical decision trees
Artificial Intelligence
An Instantiation of Hierarchical Distance-Based Conceptual Clustering for Propositional Learning
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
| Hi-index | 0.00 |
In this work we analyse the relation between hierarchical distance-based clustering and the concepts that can be obtained from the hierarchy by generalisation. Many inconsistencies may arise, because the distance and the conceptual generalisation operator are usually incompatible. To overcome this, we propose an algorithm which integrates distance-based and conceptual clustering. The new dendrograms can show when an element has been integrated to the cluster because it is near in the metric space or because it is covered by the concept. In this way, the new clustering can differ from the original one but the metric traceability is clear. We introduce three different levels of agreement between the clustering hierarchy obtained from the linkage distance and the new hierarchy, and we define properties these generalisation operators should satisfy in order to produce distance-consistent dendrograms.