Bootstrap technique in cluster analysis
Pattern Recognition
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Algorithms for clustering data
Algorithms for clustering data
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incorporating Gene Ontology in Clustering Gene Expression Data
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Fusing microarray experiments with multivariate regression
Bioinformatics
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A Hybrid DTW Based Method for Integration Analysis of Time Series Data
ICAIS '09 Proceedings of the 2009 International Conference on Adaptive and Intelligent Systems
An adaptive approach for integration analysis of multiple gene expression datasets
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Integrating heterogeneous microarray data sources using correlation signatures
DILS'05 Proceedings of the Second international conference on Data Integration in the Life Sciences
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In this article, we study two microarray data integration techniques and describe how they can be applied and validated on a set of independent, but biologically related, microarray data sets in order to derive consistent and relevant clustering results. First, we present a cluster integration approach, which combines the information containing in multiple data sets at the level of expression or similarity matrices, and then applies a clustering algorithm on the combined matrix for subsequent analysis. Second, we propose a technique for the integration of multiple partitioning results. The performance of the proposed cluster integration algorithms is evaluated on time series expression data using two clustering algorithms and three cluster validation measures. We also propose a modified version of the Figure of Merit (FOM) algorithm, which is suitable for estimating the predictive power of clustering algorithms when they are applied to multiple expression data sets. In addition, an improved version of the well-known connectivity measure is introduced to achieve a more objective evaluation of the connectivity performance of clustering algorithms.