@inproceedings{4dec08d1b7b14663a15a20e6e44a02ae,
title = "Knowledge discovery from databases with the guidance of a causal network",
abstract = "The advancement of knowledge discovery from databases (KDD) has been hampered by the problems such as the lack of statistical rigor, overabundance of patterns, and poor integration. This paper describes a new model for KDD that applies a causal network to guide the discovery processes. The new model not only allows the user to express what kind of knowledge to be discovered, but also uses the user intention to alleviate the overabundance problem. In this new model, the causal network is applied to represent the relevant variables and their relationships in the problem domain, and in due course updated according to the extracted knowledge. An interactive data mining process based on this model is described. The approach allows a knowledge discovery process to be conducted in a more controllable manner. Fundamental features of the new model are discussed, and an example is provided to illustrate the discovery processes using this model.",
keywords = "Causal networks, Goal-driven, Knowledge discovery from databases",
author = "Qiuming Zhu and Zhengxin Chen",
year = "1997",
doi = "10.1007/3-540-63614-5_39",
language = "English (US)",
isbn = "3540636145",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "401--410",
editor = "Ras, {Zbigniew W.} and Andrzej Skowron",
booktitle = "Foundations of Intelligent Systems - 10th International Symposium, ISMIS 1997, Proceedings",
note = "10th International Symposium on Methodologies for Intelligent Systems, ISMIS 1997 ; Conference date: 15-10-1997 Through 18-10-1997",
}