Journal Article10.1016/J.INS.2013.12.003
Analysing microarray expression data through effective clustering
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TL;DR: A clustering algorithm called M-CLUBS (for Microarray data CLustering Using Binary Splitting) is proposed exhibiting higher accuracy than the hierarchical ones proposed so far while allowing a faster computation with respect to partition based approaches.
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About: This article is published in Information Sciences. The article was published on 01 Mar 2014. The article focuses on the topics: Cluster analysis & CURE data clustering algorithm.
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Citations
Clustering Algorithms: Their Application to Gene Expression Data
Jelili Oyelade,Itunuoluwa Isewon,Funke Oladipupo,Olufemi Aromolaran,Efosa Uwoghiren,Faridah Ameh,Moses Achas,Ezekiel Adebiyi +7 more
TL;DR: This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.
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Feature subset selection by gravitational search algorithm optimization
TL;DR: The experimental results show that the proposed FSS-MGSA has the ability of selecting the discriminating input features correctly and can achieve high accuracy of classification, which is comparable to or better than well-known similar classifier systems.
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Multi-view cluster analysis with incomplete data to understand treatment effects
TL;DR: This work proposes an enhanced formulation for a family of multi-view co-clustering methods to cope with the missing data problem by introducing an indicator matrix whose elements indicate which data entries are observed and assessing cluster validity only on observed entries.
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Fast and effective Big Data exploration by clustering
TL;DR: By using four stages of successive refinements, CLUBS+ delivers high-quality clusters of data grouped around their centroids, working in a totally unsupervised fashion.
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A fast and accurate algorithm for unsupervised clustering around centroids
TL;DR: Results confirm that the new algorithm is fast, impervious to noise, and produces results of better quality than other algorithms, such as BOOL, BIRCH, and k-means++, even when the analyst can determine the correct number of clusters.
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