Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
Techniques for efficient empirical induction
AI '88 Proceedings of the second Australian joint conference on Artificial intelligence
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Implementing Valiant's Learnability Theory Using Random Sets
Machine Learning
Machine Learning
Learning decision lists using homogeneous rules
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Oversearching and layered search in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multivariate discretization of continuous variables for set mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Using quantitative information for efficient association rule generation
ACM SIGMOD Record
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Further Pruning for Efficient Association Rule Discovery
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Application of Pruning Techniques for Propositional Learning to Progol
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Data mining tasks and methods: scalability
Handbook of data mining and knowledge discovery
Mining and monitoring evolving data
Handbook of massive data sets
Direct Interesting Rule Generation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The Knowledge Engineering Review
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Learning morphological disambiguation rules for Turkish
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Discovering Significant Patterns
Machine Learning
Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support
Engineering Applications of Artificial Intelligence
Towards optimal k-anonymization
Data & Knowledge Engineering
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
Optimistic pruning for multiple instance learning
Pattern Recognition Letters
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Tight Optimistic Estimates for Fast Subgroup Discovery
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Cluster-grouping: from subgroup discovery to clustering
Machine Learning
Exploring ant-based algorithms for gene expression data analysis
Artificial Intelligence in Medicine
Distributed data mining: why do more than aggregating models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ACM Transactions on Knowledge Discovery from Data (TKDD)
Connection network and optimization of interest metric for one-to-one marketing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Mining frequent itemsets in large data warehouses: a novel approach proposed for sparse data sets
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Data Mining and Knowledge Discovery
Using data mining for the refresh of learning objects digital ibraries
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
A similarity measure for time, frequency, and dependencies in large-scale workloads
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Real-valued all-dimensions search: low-overhead rapid searching over subsets of attributes
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
The greedy prepend algorithm for decision list induction
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
K-optimal pattern discovery: an efficient and effective approach to exploratory data mining
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Generality is predictive of prediction accuracy
Data Mining
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Pruning derivative partial rules during impact rule discovery
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
The hows, whys, and whens of constraints in itemset and rule discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Using rules discovery for the continuous improvement of e-learning courses
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
Explaining data-driven document classifications
MIS Quarterly
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OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm's search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance.