Title :
Humoral-mediated clustering
Author :
Ahmad, Waseem ; Narayanan, Ajit
Author_Institution :
Sch. of Comput. & Math. Sci., Auckland Univ. of Technol. (AUT), Auckland, New Zealand
Abstract :
This paper describes a novel clustering algorithm inspired by the humoral-mediated response triggered by the adaptive immune system. The key humoral-mediated features of the algorithm include B-cell antibodies produced through plasma cells and memory B-cell antibodies. Affinity threshold, network threshold, death threshold and negative clonal selection threshold are also used to derive intra-cluster and inter-cluster distance metrics that result in the merging of similar clusters and removal/identification of less significant clusters (outlier detection). The performance of the clustering algorithm is tested on both synthetic and real world datasets and compared with other clustering methods.
Keywords :
artificial immune systems; pattern clustering; B-cell antibodies; adaptive immune system; affinity threshold; death threshold; humoral-mediated clustering; humoral-mediated response; intercluster distance metrics; intracluster distance metrics; negative clonal selection threshold; network threshold; Artificial neural networks; Immune system; Adaptive immune system; Clustering; Humoral-mediated immune response; Immunoinformatics; Memory Cells; Outlier Detection;
Conference_Titel :
Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-6437-1
DOI :
10.1109/BICTA.2010.5645279