Introduction

This book is organized as follows. In this Chapter, it is the brief introduction to the data mining algorithms, the advances in the technology and the outline of the recent works for the genomic analysis. In the last section, we describe briefly about the three case studies of developing tailor-made data mining algorithms for genomic analysis. The contributions of these algorithms to the genomic analysis are also described briefly in that section and in more details in their respective case study chapters. In Chapter 2, we describe about the data mining algorithms generally. In Chapter 3, we describe about the recent advances in genomic experiment techniques. In Chapter 4, we present the first case study of CLUSTAG & WCLUSTAG, which are tailor-made hierarchical clustering and graph algorithms for tag-SNP selection. In Chapter 5, the second case study of the non-parametric method of constrained unidimensional scaling for constructions of linkage disequilibrium maps is presented. In Chapter 6, we present the last case study of building of hybrid PCA-NN algorithms for continuous microarray time series. Finally, we give the conclusions and some future works based on the case studies in Chapter 7.