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The International Journal of Data Warehousing and Mining (IJDWM) [1] is a quarterly peer-reviewed academic journal covering data warehousing and data mining. It was established in 2005 and is published by IGI Global. The editor-in-chief is David Taniar (Monash University, Australia).
Online first articles 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).
Metabolomics is a very data heavy subject, and often involves sifting through massive amounts of irrelevant data before finding any conclusions. Data mining has allowed this relatively new field of medical research to grow considerably within the last decade, and will likely be the method of which new research is found within the subject. [28]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
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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).
The Wikipedia Data Mining Project's goal is to discover the internal pattern in a Wikipedia data set and explore various data mining algorithms. Cluster algorithm/s can group Wikipedia articles based on similarity, and forms thousands of data objects into an organized tree to help people view the content.
The difference between the forecast and the observations at that time is called the departure or the innovation (as it provides new information to the data assimilation process). A weighting factor is applied to the innovation to determine how much of a correction should be made to the forecast based on the new information from the observations.