Defining an informativeness metric for clustering gene expression data

Defining an informativeness metric for clustering gene expression data

MOTIVATION Unsupervised 'cluster' analysis is an invaluable tool for exploratory microarray data analysis, as it organizes the data into groups of genes or samples in which the elements share common patterns. Once the data are clustered, finding the optimal number of informative subgroups within a dataset is a problem that, while important for understanding the underlying phenotypes, is one for...

An Effective Technique for Clustering Incremental Gene Expression data

This paper presents a clustering technique (GenClus) for gene expression data which can also handle incremental data. It is designed based on density based approach. It retains the regulation information which is also the main advantage of the clustering. It uses no proximity measures and is therefore free of the restrictions offered by them. GenClus is capable of handling datasets which are up...

An improved algorithm for clustering gene expression data

MOTIVATION Recent advancements in microarray technology allows simultaneous monitoring of the expression levels of a large number of genes over different time points. Clustering is an important tool for analyzing such microarray data, typical properties of which are its inherent uncertainty, noise and imprecision. In this article, a two-stage clustering algorithm, which employs a recently propo...

Techniques for clustering gene expression data

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognise these limitations a...

Effective Clustering Algorithms for Gene Expression Data