DocumentCode
3231492
Title
Humoral-mediated clustering
Author
Ahmad, Waseem ; Narayanan, Ajit
Author_Institution
Sch. of Comput. & Math. Sci., Auckland Univ. of Technol. (AUT), Auckland, New Zealand
fYear
2010
fDate
23-26 Sept. 2010
Firstpage
1471
Lastpage
1481
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/BICTA.2010.5645279
Filename
5645279
Link To Document