Understanding the crucial role of attribute interaction in data mining

Artificial Intelligence Review

Volumn 16, Issue 3, 2001, Pages 177-199

  Freitas, Alex A ^a  

^a PONTIFICAL CATHOLIC UNIVERSITY OF PARANÁ   (Brazil)

Author keywords

Attribute interaction; Classification; Constructive induction; Data mining; Evolutionary algorithms; Inductive logic programming; Rule induction; Rule interestingness; Simpson's paradox; Small disjuncts

Indexed keywords

DATABASE SYSTEMS; EVOLUTIONARY ALGORITHMS; KNOWLEDGE BASED SYSTEMS; LOGIC PROGRAMMING;

INDUCTIVE LOGIC PROGRAMMING;

DATA MINING;

EID: 0035500276     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1011996210207     Document Type: Review

References (54)

1. A network genetic algorithm for concept learning
2. A parallel genetic algorithm for rule discovery in large databases
3. Attribute focusing: Machine-assisted knowledge discovery applied to software production process control
4. On the role of statistical significance in exploratory data analysis
5. Analysis of results
6. A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining
7. A genetic algorithm-based solution for the problem of small disjuncts
8. Small disjuncts in action: Learning to diagnose errors in the local loop of the telephone network
9. Discovering interesting patterns for investment decision making with GLOWER - A genetic learner overlaid with entropy reduction
10. Rule Induction and Instance-based Learning: A Unified Approach
11. Inductive learning in deductive databases
12. Discovering surprising patterns by detecting occurrences of simpson's paradox
13. Knowlege discovery in databases: An overviews
14. On objective measures of rule surprisingness
15. Data mining with evolutionary algorithms workshop
16. Competition-based induction of decision models from examples
17. The mind's new science: A history of the cognitive revolution
18. High performance OLAP and Data mining on parallel computers
19. Concept learning and the problem of small disjuncts
20. A genetic programming approach to constructive induction
21. A genetic constructive induction model
22. Pruning and summarizing the discovered associations
23. A theory and methodology of inductive learning
24. Conclusions
25. Estimating concept difficulty with cross entropy
26. A parallel genetic algorithm for concept learning
27. Simpson's paradox revisited
28. Knowledge discovery from data?
29. Knowledge discovery in real databases: A report on the IJCAI-89 Workshop
30. A survey of methods for scaling up inductive algorithms
31. Learning logical definitions from relations
32. Empirical learning as a function of concept character
33. Learning hard concepts through constructive induction: Framework and rationale
34. Improving the design of induction methods by analyzing algorithm functionality and data-based concept complexity
35. Some studies in machine learning using the game of checkers
36. Overfitting avoidance as bias
37. The interpretation of interaction in contingency tables
38. Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes
39. Symbolic interpretation of artificial neural networks
40. The problem of small disjuncts: Its remedy in decision trees
41. Interpretation and knowledge discovery from the multilayer perceptron network: Opening the black box
42. Simpson's paradox in real life
43. Learning with rare cases and small disjuncts
44. The problem with noise and small disjuncts
45. A quantitative study of small disjuncts
46. Special session on data mining