TY  - JOUR
AB  - While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques. © 2003 Taylor and Francis Group, LLC.
AU  - Zhang, Z
AU  - Zhang, C
AU  - Zhang, S
DA  - 2003/05/01
DO  - 10.1080/713827179
EP  - 398
JO  - Applied Artificial Intelligence
PY  - 2003/05/01
SP  - 383
TI  - An agent-based hybrid framework for database mining
VL  - 17
Y1  - 2003/05/01
Y2  - 2026/05/23
ER  -