Title :
Improving expression data mining through cluster validation
Author :
Bolshakova, N. ; Azuaje, F.
Author_Institution :
Dept. of Comput. Sci., Trinity Coll., Dublin, Ireland
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;
Conference_Titel :
Information Technology Applications in Biomedicine, 2003. 4th International IEEE EMBS Special Topic Conference on
Print_ISBN :
0-7803-7667-6
DOI :
10.1109/ITAB.2003.1222407
