Development of a hierarchical k-selecting clustering algorithm – application to allergy.
Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
The objective with this Master’s thesis was to develop, implement and evaluate an iterative procedure for hierarchical clustering with good overall performance which also merges features of certain already described algorithms into a single integrated package. An accordingly built tool was then applied to an allergen IgE-reactivity data set. The finally implemented algorithm uses a hierarchical approach which illustrates the emergence of patterns in the data. At each level of the hierarchical tree a partitional clustering method is used to divide data into k groups, where the number k is decided through application of cluster validation techniques. The cross-reactivity analysis, by means of the new algorithm, largely arrives at anticipated cluster formations in the allergen data, which strengthen results obtained through previous studies on the subject. Notably, though, certain unexpected findings presented in the former analysis where aggregated differently, and more in line with phylogenetic and protein family relationships, by the novel clustering package.
Place, publisher, year, edition, pages
Institutionen för fysik, kemi och biologi , 2007. , 43 p.
bioinformatics, partitional clustering, hierarchical clustering, allergy, crossreactivity
Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:liu:diva-10273ISRN: LITH-IFM-EX--07/1874--SEOAI: oai:DiVA.org:liu-10273DiVA: diva2:17019
2007-11-14, Planck, Fysikhuset, Linköpings universitet, Linköping, 14:15
UppsokPhysics, Chemistry, Mathematics
Hammerling, UlfSoeria-Atmadja, Daniel