Towards interactive exploration of gene expression patterns
ACM SIGKDD Explorations Newsletter
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
An Interactive Approach to Mining Gene Expression Data
IEEE Transactions on Knowledge and Data Engineering
Knowledge guided analysis of microarray data
Journal of Biomedical Informatics
Dynamic agglomerative clustering of gene expression profiles
Pattern Recognition Letters
Novel Algorithm for Coexpression Detection in Time-Varying Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bregman bubble clustering: A robust framework for mining dense clusters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Efficient layered density-based clustering of categorical data
Journal of Biomedical Informatics
A Biclustering Method to Discover Co-regulated Genes Using Diverse Gene Expression Datasets
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
A new approach for clustering gene expression time series data
International Journal of Bioinformatics Research and Applications
Split-Order Distance for Clustering and Classification Hierarchies
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
An Evolutionary Hierarchical Clustering Method with a Visual Validation Tool
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Cluster analysis on time series gene expression data
International Journal of Business Intelligence and Data Mining
Hierarchical density-based clustering of categorical data and a simplification
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An estimation of distribution algorithm for the automatic generation of clustering algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Comparative analysis of biclustering algorithms
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Mining hot clusters of similar anomalies for system management
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
CNNC: a common nearest neighbour clustering approach for gene expression data
International Journal of Computational Vision and Robotics
Isolating top-k dense regions with filtration of sparse background
Pattern Recognition Letters
International Journal of Bioinformatics Research and Applications
Clustering biological data using voronoi diagram
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
A visual analytics framework for cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
MicroClAn: Microarray clustering analysis
Journal of Parallel and Distributed Computing
An evolutionary computational model applied to cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene expression data clustering using a multiobjective symmetry based clustering technique
Computers in Biology and Medicine
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Clustering the time series gene expression data is an important task in bioinformatics research and biomedical applications. 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 clustering time series gene expression data by proposing algorithm 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 outliers of the clusters can be further investigated using the attraction tree, which is an intermediate result of the mining. 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.