C4.5: programs for machine learning
C4.5: programs for machine learning
Introduction to parallel algorithms
Introduction to parallel algorithms
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Computer Architecture and Parallel Processing
Computer Architecture and Parallel Processing
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Information-Theoretic Approach to the Pre-pruning of Classification Rules
Proceedings of the IFIP 17th World Computer Congress - TC12 Stream on Intelligent Information Processing
Using J-Pruning to Reduce Overfitting of Classification Rules in Noisy Domains
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
ScalParC: A New Scalable and Efficient Parallel Classification Algorithm for Mining Large Datasets
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Grid warehousing of molecular dynamics protein unfolding data
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
Computer Architecture, Fourth Edition: A Quantitative Approach
Computer Architecture, Fourth Edition: A Quantitative Approach
PMCRI: A Parallel Modular Classification Rule Induction Framework
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Jmax-pruning: A facility for the information theoretic pruning of modular classification rules
Knowledge-Based Systems
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
Knowledge acquisition based on learning of maximal structure fuzzy rules
Knowledge-Based Systems
Multi model transfer learning with RULES family
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.