Paper Info
Title
RFCT: An Association-Based Causality Miner
Abstract
Discovering causal relations in a system is essential to understanding how it works and to learning how to control the behaviour of the system. RFCT is a causality miner that uses association relations as the basis for the discovery of causal relations. It does so by making explicit the temporal relationships among the data. RFCT uses C4.5 as its association discoverer, and by using a series of pre-processing and post-processing techniques enables the user to try different scenarios for mining causality. The raw data to be mined should originate from a single system over time. RFCT expands the abilities of C4.5 in some important ways. It is an unsupervised tool that can handle and interpret temporal data. It also helps the user in analyzing the relationships among the variables by enabling him/her to see the rules, and statistics about them, in tabular form. The user is thus encouraged to perform experiments and discover any causal or temporal relationships among the data.
Year
DOI
Venue
2002
10.1007/3-540-47922-8_29
Canadian Conference on AI
Keywords
Field
DocType
causality miner,mining causality,association relation,association-based causality miner,association discoverer,temporal relationship,causal relation,temporal data,different scenario,raw data,single system
Data mining,Causality,Computer science,Causal relations,Raw data,Graphical user interface,Temporal database,Artificial intelligence,User interface,Machine learning,Statistical analysis
Conference
ISBN
Citations 
PageRank 
3-540-43724-X
3
0.53
References 
Authors
4
2
Name
Order
Citations
PageRank
Kamran Karimi111817.23
Howard J. Hamilton21501145.55