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
Clustering gene expression patterns
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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In recent years, the microarray techniques have received extensive attentions due to its wide applications in biomedical industry. The main advantage of microarray technique is it allows simultaneous studies of the expressions of thousands of genes in a single experiment. Analyzing the microarray data is a challenge that arises the applications of various clustering methods used for data mining. Although a number of clustering methods have been proposed, they can not meet the requirements of automation, high quality and high efficiency at the same time in analyzing gene expression data. In this paper, we propose an automatic and efficient clustering approach for mining gene expression data produced via microarray techniques. Through performance experiments on real data sets, the proposed method is shown to achieve higher efficiency, clustering quality and automation than other clustering methods.