AI Article Synopsis

  • The dynamic clonal selection immune clustering algorithm is based on clonal selection immune principles and hierarchical clustering without requiring prior knowledge.
  • The algorithm utilizes antibody affinity for antigen recognition and dynamically adjusts mutation rates, impacting antibody locations as evolution progresses.
  • Experimental results indicate that this new algorithm achieves better clustering accuracy and overall performance compared to traditional clustering methods.

Article Abstract

According to the basis of clonal selection immune algorithm and hierarchical clustering, a dynamic clonal selection immune clustering algorithm is presented, which no pre-knowledge is needed. The proposed algorithm bases on antibody affinity, to recognize antigen, restrain and merge antibody. By using aiNET immune network model, the algorithm mutates location of antibodies, in which the mutating rate is dynamically adjusted with inverse proportion to the number of immune evolution generations. After dynamic mutation, the similar antibodies are merged again, and the same processes repeats until it meets the ending condition. Experimental results showed that the proposed algorithm is more coincidental reality of clustering and more preferable performance than traditional ones.

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Source
http://dx.doi.org/10.1109/IEMBS.2008.4649339DOI Listing

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