Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MicroCluster: Efficient Deterministic Biclustering of Microarray Data
IEEE Intelligent Systems
Quick Hierarchical Biclustering on Microarray Gene Expression Data
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Shifting and scaling patterns from gene expression data
Bioinformatics
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
Discovering non-exclusive functional modules from gene expression data
International Journal of Information and Communication Technology
Clustering and classifying informative attributes using rough set theory
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Discovering biologically significant information from gene expression data is now a days playing important role in gene function detection, gene regulation, drug discovery, detecting and predicting the diseases. Many traditional clustering algorithms are present to discover such gene regulations. Such discovered clusters are known as global clusters, which incurs more processing overhead. To overcome such problem, the biclustering approach, also known as local clustering has been emerged. Generally, there are two ways of measuring the similarity among subset of objects and attributes. First one is grouping the data elements by measuring the similarity based on the proximity. But, there may be the case that, many objects and attributes which are far apart but the gives significant meaning for being grouped. This problem can be solved by the second method, which not only measures the proximity of data elements but also find subset of objects and attributes which forms similar or coherent patterns such as scaling and shifting irrespective of their proximity. In this paper, we have implemented the pattern based clustering and before that the dimensionality reduction using Principal Component Analysis (PCA) is used to reduce the attributes without loss of information. We have compared the Minimum Squared Residue (MSR) approach of Cheng and Church with our proposed model. Our method shows its better performance as compared to MSR based approach.