Dynamic rule refinement in knowledge-based data mining systems
The availability of relatively inexpensive computing power as well as the ability to obtain, store, and retrieve huge amounts of data has spurred interest in data mining. In a majority of data mining applications, most of the effort is spent in cleaning the data and extracting useful patterns in the data. However, a critical step in refining the extracted knowledge especially in dynamic environments is often overlooked. This paper focuses on knowledge refinement, a necessary process to obtain and maintain current knowledge in the domain of interest. The process of knowledge refinement is necessary not only to have accurate and effective knowledge bases but also to dynamically adapt to changes. KREFS, a knowledge refinement system, is presented and evaluated in this paper. KREFS refines knowledge by intelligently self-guiding the generation of new training examples. Avoiding typical problems associated with dependency on domain knowledge, KREFS identifies and learns distinct concepts from scratch. In addition to improving upon features of existing knowledge refinement systems, KREFS provides a general framework for knowledge refinement. Compared to other knowledge refinement systems, KREFS is shown to have more expressive power that renders its applicability in more realistic applications involving the management of knowledge.
data mining knowledge refinement