A Hybrid Approach for Learning Concept Hierarchy from Malay Text Using GAHC and Immune Network
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
A hybrid approach for learning concept hierarchy from Malay text using artificial immune network
Natural Computing: an international journal
An optimized k-means algorithm of reducing cluster intra-dissimilarity for document clustering
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
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The human immune system provides inspiration for solving a wide range of innovative problems.In this paper, we propse an immune network based approach for web document clustering.All the immune cells in the network competitively recognize the antigens (web documents) which are presented to the network one by one.The interaction between immune cells and an antigen leads to an augment of the network through the clonal selection and somatic mutation of the stimulated immune cells, while the interaction among immune cells results in a network compression.The structure of the immune network is well maintained by learning and self-regularity. We use a public web document data set to test the effectiveness of our method and compare it with other approaches.The experimental results demonstrate that the most striking advantage of immune-based data clustering is its adaptation in dynamic environment and the capability of finding new clusters automatically.