Algorithms for clustering data
Algorithms for clustering data
Clustering gene expression patterns
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Analyzing Gene Expression Time-Courses
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Clustering short time series gene expression data
Bioinformatics
A knowledge-driven approach to cluster validity assessment
Bioinformatics
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Correlation between Gene Expression and GO Semantic Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Comparative study on proximity indices for cluster analysis of gene expression time series
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - SBRN'02
Pattern Recognition Letters
Top 10 algorithms in data mining
Knowledge and Information Systems
Techniques for clustering gene expression data
Computers in Biology and Medicine
Clustering
On comparing two sequences of numbers and its applications to clustering analysis
Information Sciences: an International Journal
Clustering of gene expression data based on shape similarity
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Clustering of unevenly sampled gene expression time-series data
Fuzzy Sets and Systems
Distance functions, clustering algorithms and microarray data analysis
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A Comparative Study on the Use of Correlation Coefficients for Redundant Feature Elimination
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
The three steps of clustering in the post-genomic era: a synopsis
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
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Cluster analysis is usually the first step adopted to unveil information from gene expression microarray data. Besides selecting a clustering algorithm, choosing an appropriate proximity measure (similarity or distance) is of great importance to achieve satisfactory clustering results. Nevertheless, up to date, there are no comprehensive guidelines concerning how to choose proximity measures for clustering microarray data. Pearson is the most used proximity measure, whereas characteristics of other ones remain unexplored. In this paper, we investigate the choice of proximity measures for the clustering of microarray data by evaluating the performance of 16 proximity measures in 52 data sets from time course and cancer experiments. Our results support that measures rarely employed in the gene expression literature can provide better results than commonly employed ones, such as Pearson, Spearman, and euclidean distance. Given that different measures stood out for time course and cancer data evaluations, their choice should be specific to each scenario. To evaluate measures on time-course data, we preprocessed and compiled 17 data sets from the microarray literature in a benchmark along with a new methodology, called Intrinsic Biological Separation Ability (IBSA). Both can be employed in future research to assess the effectiveness of new measures for gene time-course data.