The local maximum clustering method and its application in microarray gene expression data analysis (Q1773693)

Summary: An unsupervised data clustering method, called the local maximum clustering (LMC) method, is proposed for identifying clusters in experimental data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distributions. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchical clustering method, the \(K\)-mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of \textit{T. R. Golub} et al. [Science 286, No. 5439, 531--537 (1999); see also SIAM Rev. 45, No. 4, 706--723 (2030; Zbl 1030.92017)].