RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Efficiently Mining Gene Expression Data via a Novel Parameterless Clustering Method
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced correlation search technique for clustering cancer gene expression data
SSIP'06 Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing
Bicluster Algorithm and Used in Market Analysis
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
Applying biclustering to text mining: an immune-inspired approach
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
An effective measure for assessing the quality of biclusters
Computers in Biology and Medicine
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Microarray data biclustering is very important for the research on gene regulatory mechanisms. Genes which exhibit similar patterns are often functionally related. In this paper a novel bicluster detection method is proposed. It makes use of one of the existing traditional clustering algorithms such as K-means as an intermediate tool to do data clustering with the submatrices created from the original data matrix. Especially, in order to save the memory storage requirement, reduce the useless clustering processing and accelerate the bicluster detection speed, a clustering and verification combined algorithm is applied. The former helps to find out the row numbers where possible biclusters lie in, while the latter efficiently speed up the detection processing. Based on a characteristic of bicluster, the biclusters are detected one by one. At the end of the paper experiment with the simulated data are presented.