We present an effective tree-based clustering technique (Gene ClusTree) for finding clusters over gene expression data. GeneClusTree attempts to find all the clusters over subspaces using a tree-based density approach by scanning the whole database in minimum possible scans and is free from the restrictions of using a normal proximity measure [1]. Effectiveness of GeneClusTree is established in terms of well known z-score measure and p-value over several real-life datasets. The p-value analysis shows that our technique is capable in detecting biologically relevant clusters from gene expression data. © 2011 Springer-Verlag.
CITATION STYLE
Sarmah, S., Sarmah, R. D., & Bhattacharyya, D. K. (2011). An effective density-based hierarchical clustering technique to identify coherent patterns from gene expression data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6634 LNAI, pp. 225–236). Springer Verlag. https://doi.org/10.1007/978-3-642-20841-6_19
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