BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast density estimation using CF-kernel for very large databases
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Advances in Distributed and Parallel Knowledge Discovery
Advances in Distributed and Parallel Knowledge Discovery
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
An Adaptive Meta-Clustering Approach: Combining the Information from Different Clustering Results
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Problems in gene clustering based on gene expression data
Journal of Multivariate Analysis
How to Control Clustering Results? Flexible Clustering Aggregation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
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In the context of genome research, the method of gene expression analysis has been used for several years. Related microarray experiments are conducted all over the world, and consequently, a vast amount of microarray data sets are produced. Having access to this variety of repositories, researchers would like to incorporate this data in their analyses to increase the statistical significance of their results. In this paper, we present a new two-phase clustering strategy which is based on the combination of local clustering results to obtain a global clustering. The advantage of such a technique is that each microarray data set can be normalized and clustered separately. The set of different relevant local clustering results is then used to calculate the global clustering result. Furthermore, we present an approach based on technical as well as biological quality measures to determine weighting factors for quantifying the local results proportion within the global result. The better the attested quality of the local results, the stronger their impact on the global result.