Vertical partitioning algorithms for database design
ACM Transactions on Database Systems (TODS)
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Principal curves: learning, design, and applications
Principal curves: learning, design, and applications
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
International Journal of Data Mining and Bioinformatics
A Knowledge Mining Approach for Effective Customer Relationship Management
International Journal of Knowledge-Based Organizations
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One of the key challenges of microarray studies is to derive biological insights from the gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the functional links among genes. However, the quality of the keyword lists significantly affects the clustering results. We compared two keyword weighting schemes: normalised z-score and term frequency inverse document frequency (TFIDF). Two gene sets were tested to evaluate the effectiveness of the weighting schemes for keyword extraction for gene clustering. Using established measures of cluster quality, the results produced from TFIDF-weighted keywords outperformed those produced from normalised z-score weighted keywords. The optimised algorithms should be useful for partitioning genes from microarray lists into functionally discrete clusters.