A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hierarchical clustering of gene expression profiles with graphics hardware acceleration
Pattern Recognition Letters
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Journal of Biomedical Informatics
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Bioinformatics
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Journal of Biomedical Informatics
On fuzzy cluster validity indices
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Computers in Biology and Medicine
An improved algorithm for clustering gene expression data
Bioinformatics
Evolutionary fuzzy cluster analysis with Bayesian validation of gene expression profiles
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary computation in bioinformatics
Kernelized fuzzy attribute C-means clustering algorithm
Fuzzy Sets and Systems
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Knowledge-Based Systems
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Journal of Biomedical Informatics
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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Knowledge-Based Systems
Knowledge-assisted recognition of cluster boundaries in gene expression data
Artificial Intelligence in Medicine
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Knowledge-Based Systems
Computers and Operations Research
An efficient greedy K-means algorithm for global gene trajectory clustering
Expert Systems with Applications: An International Journal
A new computational framework for gene expression clustering
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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A drastic improvement in the analysis of gene expression has lead to new discoveries in bioinformatics research. In order to analyse the gene expression data, fuzzy clustering algorithms are widely used. However, the resulting analyses from these specific types of algorithms may lead to confusion in hypotheses with regard to the suggestion of dominant function for genes of interest. Besides that, the current fuzzy clustering algorithms do not conduct a thorough analysis of genes with low membership values. Therefore, we present a novel computational framework called the ''multi-stage filtering-Clustering Functional Annotation'' (msf-CluFA) for clustering gene expression data. The framework consists of four components: fuzzy c-means clustering (msf-CluFA-0), achieving dominant cluster (msf-CluFA-1), improving confidence level (msf-CluFA-2) and combination of msf-CluFA-0, msf-CluFA-1 and msf-CluFA-2 (msf-CluFA-3). By employing double filtering in msf-CluFA-1 and apriori algorithms in msf-CluFA-2, our new framework is capable of determining the dominant clusters and improving the confidence level of genes with lower membership values by means of which the unknown genes can be predicted.