A Branch and Bound Incremental Conceptual Clusterer
Machine Learning
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
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
Machine Learning
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
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Adaptive Web Sites: Conceptual Cluster Mining
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
On growing better decision trees from data
On growing better decision trees from data
Finding the most interesting patterns in a database quickly by using sequential sampling
The Journal of Machine Learning Research
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Data Mining and Knowledge Discovery
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Obtaining Best Parameter Values for Accurate Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Discovering Significant Patterns
Machine Learning
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
SD-map: a fast algorithm for exhaustive subgroup discovery
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Tree2: decision trees for tree structured data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
From local to global patterns: evaluation issues in rule learning algorithms
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Local pattern detection and clustering
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Inductive querying for discovering subgroups and clusters
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Using classification to evaluate the output of confidence-based association rule mining
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Guest Editorial: Global modeling using local patterns
Data Mining and Knowledge Discovery
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We introduce the problem of cluster-grouping and show that it can be considered a subtask in several important data mining tasks, such as subgroup discovery, mining correlated patterns, clustering and classification. The algorithm CG for solving cluster-grouping problems is then introduced, and it is incorporated as a component in several existing and novel algorithms for tackling subgroup discovery, clustering and classification. The resulting systems are empirically compared to state-of-the-art systems such as CN2, CBA, Ripper, Autoclass and CobWeb. The results indicate that the CG algorithm can be useful as a generic local pattern mining component in a wide variety of data mining and machine learning algorithms.