Inductive querying for discovering subgroups and clusters

Authors:
Albrecht Zimmermann;Luc De Raedt
Affiliations:
Chair of Machine Learning, Institute of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany;Chair of Machine Learning, Institute of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany
Venue:
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Year:
2004

Citing 14
Cited 1

A database perspective on knowledge discovery

Communications of the ACM
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Transversing itemset lattices with statistical metric pruning

PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Confirmation-guided discovery of first-order rules with tertius

Machine Learning
Detecting Group Differences: Mining Contrast Sets

Data Mining and Knowledge Discovery
The CN2 Induction Algorithm

Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering

Machine Learning
Dynamic Feature Selection in Incremental Hierarchical Clustering

ECML '00 Proceedings of the 11th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Predictive Performance of Weghted Relative Accuracy

PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Rule Evaluation Measures: A Unifying View

ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
A perspective on inductive databases

ACM SIGKDD Explorations Newsletter
On growing better decision trees from data

On growing better decision trees from data
Itemset classified clustering

PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases

Cluster-grouping: from subgroup discovery to clustering

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce the problem of cluster-grouping and show that it integrates several important data mining tasks, i.e. subgroup discovery, mining correlated patterns and aspects from clustering. The problem of cluster-grouping can be regarded as a new type of inductive optimization query that asks for the k best patterns according to a convex criterion. The algorithm CG for solving cluster-grouping problems is presented and the underlying mechanisms are discussed. The approach is experimentally evaluated on a number of real-life data sets. The results indicate that the algorithm improves upon the subgroup discovery algorithm CN2-WRAcc and is competitive with the clustering algorithm CobWeb.