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Qt pronounced “cute”    is cross-platform software for creating graphical user interfaces as well as cross-platform applications that run on various software and hardware platforms such as Linux , Windows , macOS , Android or embedded systems with little or no change in the underlying codebase while still being a native application with native capabilities and speed.
Qt is currently being developed by The Qt Company , a publicly listed company, and the Qt Project under open-source governance , involving individual developers and organizations working to advance Qt. Qt is used for developing graphical user interfaces GUIs and multi-platform applications that run on all major desktop platforms and most mobile or embedded platforms.
Also still supported, for commercial users, are 5. In addition the KDE project provides unofficial support for, at least, Qt 5. In , the Qt Company estimated a community of about 1 million developers worldwide  in over 70 industries. Many notable open-source or proprietary cross-platform software are using Qt or QML :. Starting with Qt 4. Enterprise Controls  are commercially sold by The Qt Company. After Nokia opened the Qt source code to the community on Gitorious , various ports appeared.
There are also some ports of Qt that may be available, but are not supported anymore. These platforms are listed in List of platforms supported by Qt. See also there for current community support for other lesser known platforms, such as SailfishOS.
Qt is available under the following free software licenses:  GPL 2. In addition, Qt has always been available under a commercial license, like the Qt Commercial License,  that allows developing proprietary applications with no restrictions on licensing.
Qt comes with its own set of tools to ease cross-platform development, which can otherwise be cumbersome due to different set of development tools. In addition to Qt Creator, Qt provides qmake , a cross-platform build script generation tool that automates the generation of Makefiles for development projects across different platforms. There are other tools available in Qt, including the Qt Designer interface builder and the Qt Assistant help browser which are both embedded in Qt Creator , the Qt Linguist translation tool, uic user interface compiler , and moc Meta-Object Compiler.
The toolkit was called Qt because the letter Q looked appealing in Haavard’s Emacs typeface, and “t” was inspired by Xt , the X toolkit. On 20 May Troll Tech publicly released Qt 0. Trolltech used this license until version 1. Controversy erupted around when it became clear that the K Desktop Environment was going to become one of the leading desktop environments for Linux.
As it was based on Qt, many people in the free software movement worried that an essential piece of one of their major operating systems would be proprietary. With the release of version 2. Compromises were sought between KDE and Trolltech whereby Qt would not be able to fall under a more restrictive license than the QPL, even if Trolltech was bought out or went bankrupt.
At the end of , Trolltech released Qt 3. This was resolved when Trolltech released Qt 4. The GPL v3 with special exception  was later added as an added licensing option. Nokia focused on turning Qt into the main development platform for its devices, including a port to the Symbian S60 platform. Version 1. On 14 January , Qt version 4. In February , Nokia announced its decision to drop Symbian technologies and base their future smartphones on the Windows Phone platform instead and since then support for that platform has also been dropped.
Framework development of Qt 5 moved to open governance at qt-project. Aside from The Qt Company, many organizations and individuals using Qt as their development platform participate in the open development of Qt via the Qt Project. Another participator is Intel , contributing for example Wayland support. Qt Wiki provides a comprehensive list of English books about Qt. From Wikipedia, the free encyclopedia.
Object-oriented framework for software development. Not to be confused with QuickTime. See also: Qt version history. Most bitcoin forks also provide the reference GUI. Main article: List of platforms supported by Qt. See also: Qt Creator. Main article: Qt Project. Blanchette, Jasmin; Summerfield, Mark 14 February Prentice Hall. ISBN Summerfield, Mark 23 August Fitzek, Frank H.
Qt for Symbian 1st ed. Archived from the original on 19 December Summerfield, Mark 28 October Molkentin, Daniel 19 July No Starch Press. Thelin, Johan 3 August Foundations of Qt Development 1st ed. Archived from the original on 20 January Retrieved 18 June Dalheimer, Matthias January Programming with Qt 2nd ed. O’Reilly Media. Ezust, Alan; Ezust, Paul 10 September Archived from the original on 1 October Retrieved 5 August The Qt Company. Archived from the original on 22 February Ashlee Vance.
Retrieved 19 February Archived from the original on 11 December Retrieved 7 September Retrieved 8 May Retrieved 25 April Archived from the original on 12 October Retrieved 12 October Retrieved 6 December Retrieved 8 June Retrieved 15 June Archived from the original on 23 February Retrieved 14 March Archived from the original on 23 July Retrieved 14 June Retrieved 8 April Retrieved 5 September Archived from the original on 29 May Retrieved 1 March Retrieved 19 October Retrieved 28 April Retrieved 15 June — via GitHub.
Retrieved 30 June The Verge. January
Microsoft project 2013 handbook free. Project 2013 Quick Start Guide
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Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learningstatisticsand database systems. The term “data mining” is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the microsoft project 2013 handbook free mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java  which covers mostly machine learning material was originally to be named Practical machine learningand the term data mining was only added for marketing reasons.
The actual data mining task is the semi- automatic or automatic analysis of large quantities of data to extract previously microsoft project 2013 handbook free, interesting patterns microsoft project 2013 handbook free as groups of data records cluster analysisunusual records anomaly detectionand dependencies association rule miningsequential pattern mining.
This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.
For example, the data mining step might identify multiple groups in the data, which can then microsoft project 2013 handbook free used to obtain more accurate prediction results by a decision support system.
Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, although they do belong to больше на странице overall KDD process as additional steps. The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.
In contrast, data mining uses machine learning microsoft project 2013 handbook free statistical models to uncover clandestine or hidden patterns in a large volume of data.
The related terms data dredgingdata fishingand data snooping refer to the use of data mining methods to sample parts of a larger population data set that are or may be too small for reliable statistical inferences to be made about the validity of any patterns discovered.
These methods can, however, be used in creating new hypotheses to test against the larger data populations. In the s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis.
The term “data mining” was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies in The term data mining appeared around in the database community, with generally positive connotations. Other terms used include data archaeologyinformation harvestinginformation discoveryknowledge extractionetc. Gregory Piatetsky-Shapiro coined the term “knowledge discovery in databases” for the first workshop on the same topic KDD and this term became more popular in AI and machine learning community.
However, the term data mining became more popular in the business and press communities. Здесь was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, inUsama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding editor-in-chief. The journal Data Mining and Knowledge Discovery is the primary research journal of the field. The manual extraction of patterns from data has occurred for centuries.
Early methods of identifying patterns in data include Bayes’ theorem s and regression analysis s. As data sets have grown in size and complexity, direct “hands-on” data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as neural networkscluster analysisgenetic algorithms s читать далее, decision trees and decision rules microsoft project 2013 handbook freeand support vector machines s.
Data mining is the process of applying these methods with the intention of uncovering hidden patterns. It bridges the gap from applied statistics and artificial intelligence which usually provide the mathematical background to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.
Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the http://replace.me/21670.txt, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit.
A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.
Data mining involves six common classes of tasks: . Data mining can unintentionally be misused, producing results that microsoft project 2013 handbook free to be significant but which do not actually predict future behavior and cannot be reproduced on a new sample of data, therefore bearing little use.
This is sometimes caused by investigating too many hypotheses and not performing proper statistical hypothesis testing. The final step of microsoft project 2013 handbook free discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the algorithms are necessarily valid. It is common for data mining algorithms to find patterns in the training set which are not present in the general data set.
This is called overfitting. To overcome this, the evaluation uses a test microsoft project 2013 handbook free of data blue protocol game download for pc which the data mining algorithm was not trained. The learned patterns are applied to this test set, and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish “spam” from “legitimate” e-mails would be trained on a training set of sample e-mails.
Once trained, the learned patterns would microsoft project 2013 handbook free 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. Several statistical methods may be used to evaluate the algorithm, such as ROC curves. If the learned patterns do not meet the desired standards, it microsoft project 2013 handbook free 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. 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 microsoft project 2013 handbook free of information in relation to user behavior ethical and otherwise.
The ways in which data mining can be used can in some cases and contexts raise questions regarding privacylegality, and ethics. Data mining requires data preparation which uncovers information or patterns which compromise 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 по этой ссылке 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 читать далее were originally anonymous. It is recommended [ according to whom? Data may also be modified so as to become anonymous, so microsoft project 2013 handbook free 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, microsoft project 2013 handbook free bodily harm to the indicated individual. In one instance of privacy violationthe 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. However, the U. Safe Harbor Principlesdeveloped between andcurrently effectively expose European users to privacy exploitation by U. Microsoft project 2013 handbook free a consequence of Edward Snowden ‘s global surveillance disclosurethere has been increased discussion to microsoft project 2013 handbook free this agreement, as in particular the data will be fully exposed to the National Security Agencyand attempts to reach an agreement with the United States have failed.
In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices. These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places.
The HIPAA requires individuals microsoft project 2013 handbook free 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 microsoft project 2013 handbook free.
The use of data mining by the majority of businesses in the U. Under European copyright and database lawsthe mining of in-copyright works such as by web mining without the permission of the copyright owner is not legal. Where a database is pure data in Europe, it may be that there is no copyright—but database rights may exist so data mining becomes subject to intellectual property owners’ rights that are protected by the Database Directive. On the recommendation of the Hargreaves reviewthis led to the UK government to amend its copyright microsoft project 2013 handbook free in to allow content mining as a limitation and exception.
However, посмотреть больше to the restriction of the Information Society Directivethe 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. Since also Switzerland has been regulating data mining by allowing it in the research field under certain conditions laid down by art. This new article entered into force on 1 April The European Commission facilitated stakeholder discussion on text and data mining inunder the title of Licences for Europe.
US copyright lawand in particular its provision for fair useupholds the legality of content mining in America, and other fair use countries such as Israel, Taiwan and South Korea. 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 digitization 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. For more information about extracting information out of data as opposed to analyzing datasee:.
From Wikipedia, the free encyclopedia. Process of extracting and discovering patterns in large data microsoft office standard 2016 64 bit free download. Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random microsoft project 2013 handbook free Hidden Markov.
Anomaly detection. Artificial neural network. Reinforcement learning. Learning with humans. Active learning Crowdsourcing Human-in-the-loop. Model diagnostics.
Learning curve. Machine-learning venues. Related articles.
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