Picture child's feet next to pens, pencils and paper

Open Access research that is helping to improve educational outcomes for children

Strathprints makes available scholarly Open Access content by researchers in the School of Education, including those researching educational and social practices in curricular subjects. Research in this area seeks to understand the complex influences that increase curricula capacity and engagement by studying how curriculum practices relate to cultural, intellectual and social practices in and out of schools and nurseries.

Research at the School of Education also spans a number of other areas, including inclusive pedagogy, philosophy of education, health and wellbeing within health-related aspects of education (e.g. physical education and sport pedagogy, autism and technology, counselling education, and pedagogies for mental and emotional health), languages education, and other areas.

Explore Open Access education research. Or explore all of Strathclyde's Open Access research...

Multidimensional partitioning and bi-partitioning : analysis and application to gene expression datasets

Kalna, Gabriela and Vass, J. Keith and Higham, Desmond J. (2008) Multidimensional partitioning and bi-partitioning : analysis and application to gene expression datasets. International Journal of Computer Mathematics, 85 (3/4). pp. 475-485. ISSN 0020-7160

[img]
Preview
PDF (kvh.pdf)
kvh.pdf
Accepted Author Manuscript

Download (203kB) | Preview

Abstract

Eigenvectors and, more generally, singular vectors, have proved to be useful tools for data mining and dimension reduction. Spectral clustering and reordering algorithms have been designed and implemented in many disciplines, and they can be motivated from several dierent standpoints. Here we give a general, unied, derivation from an applied linear algebra perspective. We use a variational approach that has the benet of (a) naturally introducing an appropriate scaling, (b) allowing for a solution in any desired dimension, and (c) dealing with both the clustering and bi-clustering issues in the same framework. The motivation and analysis is then backed up with examples involving two large data sets from modern, high-throughput, experimental cell biology. Here, the objects of interest are genes and tissue samples, and the experimental data represents gene activity. We show that looking beyond the dominant, or Fiedler, direction reveals important information.