Generalização de regras de associação utilizando conhecimento de domínio e avaliação do conhecimento generalizado
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Summary
This work presents an approach for post-processing association rules that uses domain knowledge, expressed by taxonomies, to obtain a reduced and representative generalized association rule set, and examines the representativeness of generalized patterns.
- Type
- dissertation
- Published
- 2007-08-23
- Cited by
- 1
- References
- 195
- Access
- Open access
- OpenAlex
- https://openalex.org/W1914692994
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:177924377
Keywords
Computer science, Association rule learning, Generalization, Spurious relationship, Representativeness heuristic
References
- Improving the knowledge discovery process using ontologies
- Pruning and grouping of discovered association rules
- Interestingness and Pruning of Mined Patterns
- Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules
- Integrating and Updating Domain Knowledge with Data Mining
- Interestingness Measures for Association Patterns : A Perspective
- Rule Induction Using Information Theory
- Post-Analysis of Learned Rules
- Obtaining and Evaluating Generalized Association Rules
- Exploratory Visualization for Association Rule Rummaging
- A model of inexact reasoning in medicine
- Research and Trends in Data Mining Technologies and Applications
- Data Mining for Association Rules and Sequential Patterns
- Experiences in Building a Tool for Navigating Association Rule Result Sets
- Predictive Data Mining: A Practical Guide
- ARMiner: A data mining tool based on association rules
- Mining generalized association rules
- From Data Mining to Knowledge Discovery: An Overview
- Post-Processing of Association Rules
- Using Association Rules for Fraud Detection in Web Advertising Networks
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