C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Parallel construction of multidimensional binary search trees
ICS '96 Proceedings of the 10th international conference on Supercomputing
High performance multidimensional analysis and data mining
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Parallel Classification for Data Mining on Shared-Memory Multiprocessors
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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
Research on evidence theory decision tree adaptive website
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
| Hi-index | 0.00 |
Multidimensional aggregates are frequently computed to improve query performance in Online Analytical Processing applications. We present a new method for decision tree based classification trees using the aggregates computed in the multidimensional data model. The structure imposed on data in a explicit multidimensional storage mechanism leads to efficient dimensional operations. Decision tree based classification algorithms perform computations to find the best split point at each node of the tree. Efficient computation of the split in the decision tree can be done by using the one-dimensional aggregates if the cell values are the class-id values, and counts are maintained for each class. This is used repeatedly at the nodes of the decision tree to calculate splits and manage data. Previous parallel approaches for decision-tree based classification use sorted attribute lists and hash tables to compute the split point and split the data appropriately. The amount of data communicated is proportional to the product of number of records in the training set, and the number of dimensions, at each level of the tree, in the worst case. Parallel formulation of our approach uses data communication proportional to the product of the sum of cardinality of all dimensions and the number of non-classified nodes at each level of the tree. Communication volume is greatly reduced in our approach and is done in one phase of communication at each level of the tree, by coalescing messages. Preliminary results from our experiments on a coarse-grained, distributed memory parallel machine (IBM-SP2) show good performance.