Paper Info
Title
Clustering Microarrays with Predictive Weighted Ensembles
Abstract
Cluster ensembles seek a consensus across many individual partitions and the resulting solution is usually stable. Cluster ensembles are well suited to the analysis of DNA microarrays, where the tremendous size of the dataset can thwart the discovery of stable groups. Post processing cluster ensembles, where each individual partition is weighted according to its relative accuracy improves the performance of the ensemble whilst maintaining its stability. However, weighted cluster ensembles remain relatively unexplored, primarily because there are no common means of assessing the accuracy of individual clustering solutions. This paper describes a technique of creating weighted cluster ensembles suitable for use with microarray datasets. A regression technique is used to obtain individual cluster solutions. Each solution is then weighted according to its predictive accuracy. The consensus partition is obtained using a novel modification to the traditional k-means algorithm which further enforces the predictability of the solution. An estimate of the natural number of clusters can also be obtained using the modified k-means algorithm. Furthermore, a valuable byproduct of this weighted ensemble approach is a variable importance list. The methodology is applied on two well-known microarray datasets with promising results.
Year
DOI
Venue
2007
10.1109/CIBCB.2007.4221210
CIBCB
Keywords
Field
DocType
biology computing,genetics,pattern clustering,DNA microarrays,k-means algorithm,microarray clustering,predictive weighted ensembles,weighted cluster ensembles
Cluster (physics),Data mining,Predictability,Computer science,Artificial intelligence,Cluster analysis,k-means clustering,Regression,Computational intelligence,Multivariate statistics,Bioinformatics,Partition (number theory),Machine learning
Conference
Citations 
PageRank 
References 
1
0.36
11
Authors
2
Name
Order
Citations
PageRank
Christine Smyth170.93
Danny Coomans210519.07