MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering.

ABSTRACT:

Background

Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance.

Results

We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In

SUBMITTER: Kim EY 

PROVIDER: S-EPMC2743671 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature