Data Mining for Bioinformatics

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Does not allow reviews to be publicly displayed Only allows reviewers to display the journal they reviewed for. Click to let them know. Endorse this journal. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" emails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify.

A number of statistical methods may be used to evaluate the algorithm, such as ROC curves. If the learned patterns do not meet the desired standards, subsequently it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge. JDM 2.


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As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover for example subspace clustering have been proposed independently of the DMG. Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance. While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to peoples' behavior ethical and otherwise.

The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. Data mining requires data preparation which uncovers information or patterns which compromises confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together possibly from various sources in a way that facilitates analysis but that also might make identification of private, individual-level data deducible or otherwise apparent.

The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous. It is recommended [ according to whom? Data may also be modified so as to become anonymous, so that individuals may not readily be identified. The inadvertent revelation of personally identifiable information leading to the provider violates Fair Information Practices.

This indiscretion can cause financial, emotional, or bodily harm to the indicated individual. In one instance of privacy violation, the patrons of Walgreens filed a lawsuit against the company in for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies.

Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers.

Data Mining in Bioinformatics : Jason T. L. Wang :

However, the U. Safe Harbor Principles currently effectively expose European users to privacy exploitation by U. As a consequence of Edward Snowden 's global surveillance disclosure , there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency , and attempts to reach an agreement have failed. The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses.

More importantly, the rule's goal of protection through informed consent is approach a level of incomprehensibility to average individuals.

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Use of data mining by the majority of businesses in the U. Due to a lack of flexibilities in European copyright and database law , the mining of in-copyright works such as web mining without the permission of the copyright owner is not legal. Where a database is pure data in Europe there is likely to be no copyright, but database rights may exist so data mining becomes subject to regulations by the Database Directive. On the recommendation of the Hargreaves review this led to the UK government to amend its copyright law in [37] to allow content mining as a limitation and exception.

Only the second country in the world to do so after Japan, which introduced an exception in for data mining.

Data Mining in Bioinformatics

However, due to the restriction of the Copyright Directive , the UK exception only allows content mining for non-commercial purposes. UK copyright law also does not allow this provision to be overridden by contractual terms and conditions. The European Commission facilitated stakeholder discussion on text and data mining in , under the title of Licences for Europe. By contrast to Europe, the flexible nature of US copyright law, and in particular fair use means that content mining in America, as well as other fair use countries such as Israel, Taiwan and South Korea is viewed as being legal.

As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitisation project of in-copyright books was lawful, in part because of the transformative uses that the digitization project displayed - one being text and data mining.

Public access to application source code is also available. Data mining is about analyzing data; for information about extracting information out of data, see:. From Wikipedia, the free encyclopedia. Machine learning and data mining Problems. Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov.

Anomaly detection. Artificial neural networks.

Reinforcement learning. Machine-learning venues. Glossary of artificial intelligence. Related articles. List of datasets for machine-learning research Outline of machine learning. This section is missing information about non-classification tasks in data mining. It only covers machine learning.

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Please expand the section to include this information. Further details may exist on the talk page.

DATA MINING 4 Pattern Discovery in Data Mining 5 1 Sequential Pattern and Sequential Pattern Mi

September Main article: Examples of data mining. See also: Category:Applied data mining. See also: Category:Data mining and machine learning software. Analytics Behavior informatics Big data Bioinformatics Business intelligence Data analysis Data warehouse Decision support system Domain driven data mining Drug discovery Exploratory data analysis Predictive analytics Web mining. Data integration Data transformation Electronic discovery Information extraction Information integration Named-entity recognition Profiling information science Psychometrics Social media mining Surveillance capitalism Web scraping.

Retrieved Archived from the original on Data Mining: Concepts and Techniques 3rd ed. Morgan Kaufmann. Retrieved 17 December Data mining: concepts and techniques. Journal of Machine Learning Research. The term "data mining" was [added] primarily for marketing reasons. Data mining in business services. Service Business , 1 3 , The Review of Economics and Statistics. Introduction to Data Mining. KD Nuggets.


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