Interface support for data archeology
CIKM '93 Proceedings of the second international conference on Information and knowledge management
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
| Hi-index | 0.00 |
Civilian and military organizations are collecting huge amounts of data from a variety of sources. In order to make intelligent use of this data possible, researchers have developed countless data mining (or Knowledge Discovery in Databases (KDD)) systems that seek to aid in the task of extracting valid, novel, and interesting information from these large data repositories. Unfortunately, existing KDD systems have several shortcomings. One broad class of these shortcomings arises from the fact that these systems lack the means to actively collaborate with the user over extended periods of time. This paper describes our effort to address this problem by tightly coupling the knowledge discovery process with an explicit conceptual model. Central to our research is an investigation into the relationship between knowledge and discovery. More specifically, we are examining how a stored dynamic conceptual model can be used to improve KDD goal/query specification, algorithm efficiency, and results reporting.