Conference Paper

DHC: A Density-Based Hierarchical Clustering Method for Time Series Gene Expression Dat.

Dept. of Comput. Sci., State Univ. of New York, Buffalo, NY, USA;
DOI: 10.1109/BIBE.2003.1188978 Conference: 3rd IEEE International Symposium on BioInformatics and BioEngineering (BIBE 2003), 10-12 March 2003, Bethesda, MD, USA
Source: DBLP

ABSTRACT terns in underlying data, have proved to be useful in finding co-expressed genes. Clustering the time series gene expression data is an im-portant task in bioinformatics research and biomedical ap-plications. Recently, some clustering methods have been adapted or proposed. However, some concerns still remain, such as the robustness of the mining methods, as well as the quality and the interpretability of the mining results. In this paper, we tackle the problem of effectively clus-tering time series gene expression data by proposing al-gorithm DHC, a density-based, hierarchical clustering method. We use a density-based approach to identify the clusters such that the clustering results are of high quality and robustness. Moreover, The mining result is in the form of a density tree, which uncovers the embedded clusters in a data set. The inner-structures, the borders and the out-liers of the clusters can be further investigated using the attraction tree, which is an intermediate result of the min-ing. By these two trees, the internal structure of the data set can be visualized effectively. Our empirical evaluation using some real-world data sets show that the method is effective, robust and scalable. It matches the ground truth provided by bioinformatics experts very well in the sample data sets.

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