An efficient data structure for decision rules discovery

Authors:
Raúl Giráldez;Jesús S. Aguilar-Ruiz;José C. Riquelme
Affiliations:
University of Seville, Avda. Reina Mercedes S/N, 41012 Seville, Spain;University of Seville, Avda. Reina Mercedes S/N, 41012 Seville, Spain;University of Seville, Avda. Reina Mercedes S/N, 41012 Seville, Spain
Venue:
Proceedings of the 2003 ACM symposium on Applied computing
Year:
2003

Citing 15
Cited 1

The LSD tree: spatial access to multidimensional and non-point objects

VLDB '89 Proceedings of the 15th international conference on Very large data bases
Rule induction with CN2: some recent improvements

EWSL-91 Proceedings of the European working session on learning on Machine learning
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
Using Genetic Algorithms for Concept Learning

Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning

Machine Learning - Special issue on genetic algorithms
A general solution of the n-dimensional B-tree problem

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Multidimensional access methods

ACM Computing Surveys (CSUR)
The Grid File: An Adaptable, Symmetric Multikey File Structure

ACM Transactions on Database Systems (TODS)
Data Structures for Range Searching

ACM Computing Surveys (CSUR)
The K-D-B-tree: a search structure for large multidimensional dynamic indexes

SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching

SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Spatial Search with Polyhedra

Proceedings of the Sixth International Conference on Data Engineering
Efficient C4.5

Efficient C4.5
Cached sufficient statistics for efficient machine learning with large datasets

Journal of Artificial Intelligence Research
Rule induction and instance-based learning a unified approach

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2

Thesis: improving the performance of evolutionary algorithms for decision rule learning

AI Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

The increasing amount of information available is encouraging the search for efficient techniques to improve the data mining methods, especially those which consume great computational resources. We present a novel structure, called EES, which helps the data mining algorithms which generate decision rules to reduce the aforementioned cost. Given that decision rules establish conditions for database attributes, EES stores the information in such a way that the search can be carried out by attributes instead of by examples. EES could be useful for any method which generates decision rules. Moreover, it is of particular interest when the search for the solution involves a great many hypothetical solutions. Thus, this structure is designed for speeding up the rule-evaluation process in methods based on Evolutionary Algorithms. The traditional structure, based on vectors of examples (in which the database is stored) is evaluated and compared with EES, including the costs for a stratified set of cases. Finally, the experimental results demonstrate the quality of our proposal, reducing the computational cost by approximately 50%.