A Novel Swarm Clustering Algorithm and its Application for CBR Retrieval

In CBR system, the case base is becoming increasingly larger with the incremental learning which results in the decline of case retrieval efficiency and its weaker performance. Aiming at such weakness of CBR system, this article proposes a novel case retrieval method based on Hybrid Ant-Fish Cluster...

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Bibliographic Details
Main Authors: Zhe-jing Huang, Bin-qiang Wang
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Clustering algorithms
Convergence
Marine animals
Optimization
Search problems
Online Access:Request full text
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Summary:In CBR system, the case base is becoming increasingly larger with the incremental learning which results in the decline of case retrieval efficiency and its weaker performance. Aiming at such weakness of CBR system, this article proposes a novel case retrieval method based on Hybrid Ant-Fish Clustering Algorithm (HA-FC). At beginning of algorithm, we get rough cluster sets utilizing the advantage of Artificial Fish-school Algorithm which is insensitive to initial value and has high speed of searching optimizing. Then we use Ant Colony Optimization introduced the concept of Crowded Degree to avoid convergence too early and improve the ability of searching optimizing. Finally, apply this algorithm to case retrieval in order to reduce searching time and improve searching accuracy. The results of simulation demonstrate the effectiveness of this algorithm.
ISSN:2156-7379
2156-7387
DOI:10.1109/ICIECS.2010.5678408