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Data binning, also called data discrete binning or data bucketing, is a data pre-processing technique used to reduce the effects of minor observation errors. The original data values which fall into a given small interval, a bin , are replaced by a value representative of that interval, often a central value ( mean or median ).
In metagenomics, binning is the computational process of grouping assembled contigs and assigning them to their separate genomes of origin. Binning methods can be based on either compositional sequence features (such as GC-content or tetranucleotide frequencies) or sequence read mapping coverage across samples, or both. [1]
The data generated by metagenomics experiments are both enormous and inherently noisy, containing fragmented data representing as many as 10,000 species. [1] The sequencing of the cow rumen metagenome generated 279 gigabases , or 279 billion base pairs of nucleotide sequence data, [ 28 ] while the human gut microbiome gene catalog identified 3. ...
Typically data is discretized into partitions of K equal lengths/width (equal intervals) or K% of the total data (equal frequencies). [1] Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method, [2] which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others [3]
Data Mining and Knowledge Discovery is a bimonthly peer-reviewed scientific journal focusing on data mining published by Springer Science+Business Media.It was started in 1996 and launched in 1997 by Usama Fayyad as founding Editor-in-Chief by Kluwer Academic Publishers (later becoming Springer).
Data binning: a data pre-processing technique. Binning (metagenomics): the process of classifying reads into different groups or taxonomies. Product binning: in semiconductor device fabrication, the process of categorizing finished products. Pixel binning: the process of combining charge from adjacent pixels in a CCD image sensor during readout.
As most tree based algorithms use linear splits, using an ensemble of a set of trees works better than using a single tree on data that has nonlinear properties (i.e. most real world distributions). Working well with non-linear data is a huge advantage because other data mining techniques such as single decision trees do not handle this as well.
BioData Mining is a peer-reviewed open access scientific journal covering data mining methods applied to computational biology and medicine established in 2008. It is published by BioMed Central and the editors-in-chief are Jason H. Moore and Nicholas Tatonetti (Cedars Sinai Medical Center).