A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Algorithms for clustering data
Algorithms for clustering data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Visual cluster validity for prototype generator clustering models
Pattern Recognition Letters
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Novel Algorithm for Coexpression Detection in Time-Varying Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points
Pattern Recognition Letters
Efficient mining of multilevel gene association rules from microarray and gene ontology
Information Systems Frontiers
Expert Systems with Applications: An International Journal
An incremental affinity propagation algorithm and its applications for text clustering
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Constrained clustering for gene expression data mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Efficient gene selection with rough sets from gene expression data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Chinese web comments clustering analysis with a two-phase method
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Novel clustering algorithms based on improved artificial fish swarm algorithm
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
A novel two-level clustering method for time series data analysis
Expert Systems with Applications: An International Journal
Incorporating biological knowledge into density-based clustering analysis of gene expression data
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Noise reduction of cDNA microarray images using complex wavelets
IEEE Transactions on Image Processing
Hybrid data mining approaches for prevention of drug dispensing errors
Journal of Intelligent Information Systems
Isolating top-k dense regions with filtration of sparse background
Pattern Recognition Letters
A novel approach for effective learning of cluster structures with biological data applications
VDMB'06 Proceedings of the First international conference on Data Mining and Bioinformatics
A novel clustering and verification based microarray data bi-clustering method
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
MicroClAn: Microarray clustering analysis
Journal of Parallel and Distributed Computing
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Clustering analysis has been an important research topic in the machine learning field due to the wide applications. In recent years, it has even become a valuable and useful tool for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in meeting the requirements of automation, high quality, and high efficiency at the same time. In this paper, we propose a novel, parameterless and efficient clustering algorithm, namely, Correlation Search Technique (CST), which fits for analysis of gene expression data. The unique feature of CST is it incorporates the validation techniques into the clustering process so that high quality clustering results can be produced on the fly. Through experimental evaluation, CST is shown to outperform other clustering methods greatly in terms of clustering quality, efficiency, and automation on both of synthetic and real data sets.