Improving expression data mining through cluster validation

Author

Bolshakova, N. ; Azuaje, F.

Author_Institution

Dept. of Comput. Sci., Trinity Coll., Dublin, Ireland

fYear

2003

fDate

24-26 April 2003

Firstpage

19

Lastpage

22

Abstract

Presents several cluster evaluation techniques for gene expression data analysis. Normalisation and validity aggregation strategies are proposed to improve the prediction of the number of relevant clusters. The effect of different intracluster and intercluster distances on this prediction process is studied. This approach is applied to a publicly released medulloblastomas tumour data set The results suggest that it may represent an effective tool to support biomedical knowledge discovery tasks based on gene expression data.

Keywords

biology computing; data mining; genetics; medical signal processing; pattern clustering; tumours; biomedical knowledge discovery tasks; cluster evaluation techniques; cluster validation; expression data mining; gene expression data analysis; intercluster distances; intracluster distances; normalisation; publicly released medulloblastomas tumour data set; validity aggregation strategies; Algorithm design and analysis; Biomedical measurements; Clustering algorithms; Computer science; DNA; Data analysis; Data mining; Gene expression; Pharmaceutical technology; Tumors;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology Applications in Biomedicine, 2003. 4th International IEEE EMBS Special Topic Conference on

Print_ISBN

0-7803-7667-6

Type

conf

DOI

10.1109/ITAB.2003.1222407

Filename

1222407