Speedup Versus Efficiency in Parallel Systems
IEEE Transactions on Computers
Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Fast discovery of association rules
Advances in knowledge discovery and data mining
Accurately Selecting Block Size at Runtime in Pipelined Parallel Programs
International Journal of Parallel Programming
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Parallel Data Mining for Association Rules
IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations
Machine Learning
Adaptive Scheduling for Master-Worker Applications on the Computational Grid
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
Involving Aggregate Functions in Multi-relational Search
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
How to Upgrade Propositional Learners to First Order Logic: A Case Study
Machine Learning and Its Applications, Advanced Lectures
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Concurrent Execution of Optimal Hypothesis Search for Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Accelerating the Drug Design Process through Parallel Inductive Logic Programming Data Mining
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Inductive inference of VL decision rules
ACM SIGART Bulletin
An approach to mining the multi-relational imbalanced database
Expert Systems with Applications: An International Journal
A new model of evaluating concept similarity
Knowledge-Based Systems
An algorithm of constructing concept lattices for CAT with cognitive diagnosis
Knowledge-Based Systems
Conceptual modeling rules extracting for data streams
Knowledge-Based Systems
Real formal concept analysis based on grey-rough set theory
Knowledge-Based Systems
A phenotypic genetic algorithm for inductive logic programming
Expert Systems with Applications: An International Journal
ILP-based concept discovery in multi-relational data mining
Expert Systems with Applications: An International Journal
Confidence-Based Concept Discovery in Relational Databases
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
ILP with noise and fixed example size: a Bayesian approach
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Classification rule discovery for the aviation incidents resulted in fatality
Knowledge-Based Systems
Integrating induction and abduction in logic programming
Information Sciences: an International Journal
Relation between concept lattice reduction and rough set reduction
Knowledge-Based Systems
Generating C4.5 production rules in parallel
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
A comparative study on ILP-based concept discovery systems
Expert Systems with Applications: An International Journal
Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis
Knowledge-Based Systems
A probabilistic interpretation of precision, recall and F-score, with implication for evaluation
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Fitness function based on binding and recall rate for genetic inductive logic programming
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
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Due to the increase in the amount of relational data that is being collected and the limitations of propositional problem definition in relational domains, multi-relational data mining has arisen to be able to extract patterns from relational data. In order to cope with intractably large search space and still to be able to generate high-quality patterns, ILP-based multi-relational data mining and concept discovery systems employ several search strategies and pattern limitations. Another direction to cope with the large search space is using parallelization. By parallel data mining, improvement in time efficiency and scalability can be provided without further limiting the language patterns. In this work, we describe a method for concept discovery with parallelization on an ILP-based concept discovery system. The non-parallel algorithm, namely Concept Rule Induction System (CRIS), is modified in such a way that the parts that involve high amount of query processing, which causes bottleneck, are reorganized in a data parallel way. The resulting algorithm is called, Parallel CRIS (pCRIS). A set of experiments is conducted in order to evaluate the performance of the proposed method.