@article{2952,
  author = {Abdelhak Mansoul, Baghdad Atmani},
  title = {Case Retrieval for CBR based on Clustering to Support Decision},
  journal = {Journal of Networking Technology},
  year = {2020},
  volume = {11},
  number = {1},
  doi = {https://doi.org/10.6025/jnt/2020/11/1/9-24},
  url = {http://www.dline.info/jnt/fulltext/v11n1/jntv11n1_2.pdf},
  abstract = {Case-based reasoning has been widely adopted for decision support. A major operation in the CBR is the
retrieval of similar cases. However, this operation has some weaknesses. One among them is the retrieval of several similar cases. On the other hand, sequentially processing all cases with a similarity has a complexity in presence of a great case base and several features. So, improving retrieval has been focused by a considerable amount studies using sequential calculation, non-sequential indexing, classification algorithms and Nearest Neighbor matching, etc., while others use hybridization of CBR with other reasoning methodologies. In this paper, we propose a novel approach based on CBR and clustering to improve the retrieval operation, and impacts positively the whole reasoning process. Our aim is to propose an available strategy for the retrieval task and also a valid decision support model. Finally, we present preliminary results and suggestions to extend our approach.},
}