Although very widely used in unsupervised data mining, most clustering methods are affected by the instability of the resulting clusters w.r.t. the initialization of the algorithm ...
—In this paper, we have modified a constrained clustering algorithm to perform exploratory analysis on gene expression data using prior knowledge presented in the form of constr...
Erliang Zeng, Chengyong Yang, Tao Li, Giri Narasim...
Spectral clustering is useful for a wide-ranging set of applications in areas such as biological data analysis, image processing and data mining. However, the computational and/or...
Ling Huang, Donghui Yan, Michael I. Jordan, Nina T...
Clustering is often formulated as the maximum likelihood estimation of a mixture model that explains the data. The EM algorithm widely used to solve the resulting optimization pro...
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression pa...