The Pronk Pops Show 883, April 28, 2017, Breaking: Story 1: North Korea Launches Missile Into Sea of Japan — Blows Up — Videos — Story 2: U.S. Government Remains Open Spending Your Taxes and Piling On More Debt — No Democratic Shutdown and No Fiscal Year 2017 Funding of Trump’s Wall –Videos — Story 3: Do Not Be Fooled — National Security Agency Collects and Stores Everything Going Over The Internet and Telephone Switching Network (Bulk Acquisition) — Any American Can Be National Security Target With Dossier Built From Data Mined NSA Data Sets — Targeted: President Trump, President Elect Trump and Candidate Trump — No Warrants — Videos — Story 4: U.S. Economic Growth Stagnating — Videos

Posted on April 29, 2017. Filed under: Blogroll, Breaking News, Communications, Congress, Corruption, Countries, Donald J. Trump, Donald J. Trump, Donald Trump, Donald Trump, Education, Empires, Employment, Government Dependency, House of Representatives, Law, News, Nuclear Weapons, Philosophy, Photos, Politics, President Trump, Raymond Thomas Pronk, Rule of Law, Scandals, Senate, Taxation, Taxes, Terror, Terrorism, United States of America, Wealth, Wisdom | Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |

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The Pronk Pops Show Podcasts

Pronk Pops Show 883 April 28, 2017

Pronk Pops Show 882: April 27, 2017

Pronk Pops Show 881: April 26, 2017

Pronk Pops Show 880: April 25, 2017

Pronk Pops Show 879: April 24, 2017

Pronk Pops Show 878: April 21, 2017

Pronk Pops Show 877: April 20, 2017

Pronk Pops Show 876: April 19, 2017

Pronk Pops Show 875: April 18, 2017

Pronk Pops Show 874: April 17, 2017

Pronk Pops Show 873: April 13, 2017

Pronk Pops Show 872: April 12, 2017

Pronk Pops Show 871: April 11, 2017

Pronk Pops Show 870: April 10, 2017

Pronk Pops Show 869: April 7, 2017

Pronk Pops Show 868: April 6, 2017

Pronk Pops Show 867: April 5, 2017

Pronk Pops Show 866: April 3, 2017

Pronk Pops Show 865: March 31, 2017

Pronk Pops Show 864: March 30, 2017

Pronk Pops Show 863: March 29, 2017

Pronk Pops Show 862: March 28, 2017

Pronk Pops Show 861: March 27, 2017

Pronk Pops Show 860: March 24, 2017

Pronk Pops Show 859: March 23, 2017

Pronk Pops Show 858: March 22, 2017

Pronk Pops Show 857: March 21, 2017

Pronk Pops Show 856: March 20, 2017

Pronk Pops Show 855: March 10, 2017

Pronk Pops Show 854: March 9, 2017

Pronk Pops Show 853: March 8, 2017

Pronk Pops Show 852: March 6, 2017

Pronk Pops Show 851: March 3, 2017

Pronk Pops Show 850: March 2, 2017

Pronk Pops Show 849: March 1, 2017

Pronk Pops Show 848: February 28, 2017

Pronk Pops Show 847: February 27, 2017

Pronk Pops Show 846: February 24, 2017

Pronk Pops Show 845: February 23, 2017

Pronk Pops Show 844: February 22, 2017

Pronk Pops Show 843: February 21, 2017

Pronk Pops Show 842: February 20, 2017

Pronk Pops Show 841: February 17, 2017

Pronk Pops Show 840: February 16, 2017

Pronk Pops Show 839: February 15, 2017

Pronk Pops Show 838: February 14, 2017

Pronk Pops Show 837: February 13, 2017

Pronk Pops Show 836: February 10, 2017

Pronk Pops Show 835: February 9, 2017

Pronk Pops Show 834: February 8, 2017

Pronk Pops Show 833: February 7, 2017

Pronk Pops Show 832: February 6, 2017

Pronk Pops Show 831: February 3, 2017

Pronk Pops Show 830: February 2, 2017

Pronk Pops Show 829: February 1, 2017

Pronk Pops Show 828: January 31, 2017

Pronk Pops Show 827: January 30, 2017

Pronk Pops Show 826: January 27, 2017

Pronk Pops Show 825: January 26, 2017

Pronk Pops Show 824: January 25, 2017

Pronk Pops Show 823: January 24, 2017

Pronk Pops Show 822: January 23, 2017

Pronk Pops Show 821: January 20, 2017

Pronk Pops Show 820: January 19, 2017

Pronk Pops Show 819: January 18, 2017

Pronk Pops Show 818: January 17, 2017

Pronk Pops Show 817: January 13, 2017

Pronk Pops Show 816: January 12, 2017

Pronk Pops Show 815: January 11, 2017

Pronk Pops Show 814: January 10, 2017

Pronk Pops Show 813: January 9, 2017

Image result for cartoons north korea missiles

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North Korea launches new ballistic missile – 28 April 2017

Secretary Of State – Rex Tillerson Full Speech On North Korea at UN Security Council

Sec. Rex Tillerson Speech About North Korea At U.N. April 28. 2017.

N. Korean leader doesn′t want to end up like Gaddafi: WSJ ″김정은, 카다피처럼 죽기

Newly released footage of North Korea massive military drills

BRING IT ON, KIM

US threatens decisive response as North Korea fires ANOTHER failed ballistic missile just hours after Donald Trump warned of ‘major conflict’ with rogue state

Medium range rocket – which may have been nuclear capable – crashed down into the Sea of Japan minutes after launch

THE US threatened a swift response after North Korea fired another ballistic missile last night, ratcheting up tensions with the West.

Trump administration chiefs said a raft of new economic sanctions and military manoeuvres may be speedily deployed after the rocket threat.

A North Korean ballistic missile is fired from a submarine during an earlier rocket test this week

A North Korean ballistic missile is fired from a submarine during an earlier rocket test this week

North Korea fired a volley of missiles towards Japan in March. Tensions with the West have reached fever pitch over recent weeks

North Korea fired a volley of missiles towards Japan in March. Tensions with the West have reached fever pitch over recent weeks

US Government source says North Korea’s missile test was unsuccessful

North Korea disrespected the wishes of China & its highly respected President when it launched, though unsuccessfully, a missile today. Bad!

Earlier the President warned of a “major, major conflict” with Kim Jong-un’s hermit state if it doesn’t fall into line.

And China ordered it to stand down its rocket testing or risk harsh sanctions.

In a failed test, a missile was launched from Pukchang Airfield north of the capital Pyongyang at around 9:30pm GMT.

US officials confirmed they had tracked the medium range weapon, which may have been nuclear capable, and that President Trump had been informed.

He later tweeted: “North Korea disrespected the wishes of China & its highly respected President when it launched, though unsuccessfully, a missile today. Bad!”

US Secretary of State Rex Tillerson calls for united actions against North Korea at UNThe rocket exploded within North Korean territory above the sea of Japan minutes after it was fired, military chiefs said.

“It’s possible that something could be sped up,” a senior White House official said of the potential for imposing new sanctions on North Korea.

“Something that’s ready to go could be taken from the larger package and expedited.”

Just hours earlier, US Secretary of State Rex Tillerson warned that failure to stop North Korea’s nuclear and missile development could lead to “catastrophic consequences”.

China, which has long provided a lifeline to the rogue nation, upped its effort to rein in its trigger-happy neighbour by threatening economic curbs if it further posed a risk.

But the Chinese echoed the Russians by cautioning the West against threatening brute force against skittish Kim.

Donald Trump, pictured travelling to a National Rifle Association meeting on Friday, was informed of the launch

Donald Trump, pictured travelling to a National Rifle Association meeting on Friday, was informed of the launch

US Secretary of State Rex Tillerson warned the UN on Friday of 'catastrophic consequences' if North Korea wasn't stopped

US Secretary of State Rex Tillerson warned the UN on Friday of ‘catastrophic consequences’ if North Korea wasn’t stopped

North Korea's barmy leader Kim Jong-un, seen attending a weapons demonstration this week, risks sparking war

North Korea’s barmy leader Kim Jong-un, seen attending a weapons demonstration this week, risks sparking war

https://www.thesun.co.uk/news/3441785/north-korea-missile-test-donald-trump-response/

Congress passes short-term fix to avoid gov’t shutdown

NSA Whistleblower Bill Binney on Tucker Carlson 03.24.2017

Nunes says intel community collected info on Trump transition team

William Binney – NSA Domestic Data Mining

The National Security Agency campus in Fort Meade, Md. CreditPatrick Semansky/Associated Press

WASHINGTON — The National Security Agency has halted one of the most disputed practices of its warrantless wiretapping program: collecting Americans’ emails and texts to and from people overseas that mention foreigners targeted for surveillance, according to officials familiar with the matter.

National security officials have argued that such surveillance is lawful and helpful in identifying people who might have links to terrorism, espionage or otherwise are targeted for intelligence-gathering. The fact that the sender of such a message would know an email address or phone number associated with a surveillance target is grounds for suspicion, these officials argued.

The decision is a major development in American surveillance policy. It brings to an end a once-secret form of wiretapping that privacy advocates have argued overstepped the Fourth Amendment’s ban on unreasonable searches — even though the Foreign Intelligence Surveillance Court upheld it as lawful — because the government was intercepting communications based on what they say, rather than who sent or received them.

Senator Ron Wyden, an Oregon Democrat who has long been a critic of N.S.A. surveillance, said that he would introduce legislation codifying the new limit. The law that authorizes the program, the FISA Amendments Act, is up for renewal at the end of 2017.

“This change ends a practice that allowed Americans’ communications to be collected without a warrant merely for mentioning a foreign target,” Mr. Wyden said. “For years I’ve repeatedly raised concerns that this amounted to an end-run around the Fourth Amendment. This transparency should be commended.”

The existence of this so-called “about the target” collection was first reported by The New York Times in 2013.

The N.S.A. made the change to resolve problems it was having complying with special rules imposed by the surveillance court in 2011 to protect Americans’ privacy. For technical reasons, the agency ended up collecting messages sent and received domestically as a byproduct of such surveillance, the officials said.

The problem stemmed from certain bundled messages that internet companies sometimes packaged together and transmitted as a unit. If even one of them had a foreign target’s email address somewhere in it, all were sucked in.

After the N.S.A. brought that issue to the court’s attention in 2011, a judge ruled that it violated the Fourth Amendment, which bars unreasonable searches. The agency then proposed putting the bundled messages in a special repository to which analysts, searching through intercepts to write intelligence reports, would generally not have access. The court permitted that type of collection to continue with that restriction.

But last year, officials said, the N.S.A. discovered that analysts were querying the bundled messages in a way that did not comply with those rules. The agency brought the matter to the court’s attention, resulting in a delay in reauthorizing the broader warrantless surveillance program until the agency proposed ceasing this collection practice.

https://pronkpops.wordpress.com/wp-admin/post.php?post=25210&action=edit

Data mining

From Wikipedia, the free encyclopedia
Not to be confused with analytics, information extraction, or data analysis.

Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.[1] It is an interdisciplinary subfield of computer science.[1][2][3] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD.[4]

The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[5] It also is a buzzword[6]and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[7] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[8]Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.

The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential 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 be 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, but do belong to the overall KDD process as additional steps.

The related terms data dredging, data fishing, and 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.

Etymology

In the 1960s, statisticians 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” appeared around 1990 in the database community. For a short time in 1980s, a phrase “database mining”™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation;[9] researchers consequently turned to “data mining”. Other terms used include Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction, etc. Gregory Piatetsky-Shapiro coined the term “Knowledge Discovery in Databases” for the first workshop on the same topic (KDD-1989) 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.[10] Currently, Data Mining and Knowledge Discovery are used interchangeably.

In the Academic community, the major forums for research started in 1995 when the First International Conference on Data Mining and Knowledge Discovery (KDD-95) was started in Montreal under AAAI sponsorship. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, in 1996, Usama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding Editor-in-Chief. Later he started the SIGKDDD Newsletter SIGKDD Explorations.[11] The KDD International conference became the primary highest quality conference in Data Mining with an acceptance rate of research paper submissions below 18%. The Journal Data Mining and Knowledge Discovery is the primary research journal of the field.

Background

The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes’ theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation ability. 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, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns[12] in large data sets. 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.

Process

The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:

(1) Selection
(2) Pre-processing
(3) Transformation
(4) Data Mining
(5) Interpretation/Evaluation.[4]

It exists, however, in many variations on this theme, such as the Cross Industry Standard Process for Data Mining (CRISP-DM) which defines six phases:

(1) Business Understanding
(2) Data Understanding
(3) Data Preparation
(4) Modeling
(5) Evaluation
(6) Deployment

or a simplified process such as (1) pre-processing, (2) data mining, and (3) results validation.

Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners.[13] The only other data mining standard named in these polls was SEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models,[14][15] and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.[16]

Pre-processing

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 data, 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

Data mining involves six common classes of tasks:[4]

  • Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
  • Association rule learning (Dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
  • Clustering – is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
  • Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”.
  • Regression – attempts to find a function which models the data with the least error.
  • Summarization – providing a more compact representation of the data set, including visualization and report generation.

Results validation

An example of data produced by data dredging through a bot operated by statistician Tyler Vigen, apparently showing a close link between the best word winning a spelling bee competition and the number of people in the United States killed by venomous spiders. The similarity in trends is obviously a coincidence.

Data mining can unintentionally be misused, and can then produce results which appear to be significant; but which do not actually predict future behaviour and cannot be reproduced on a new sample of data and bear little use. Often this results from investigating too many hypotheses and not performing proper statistical hypothesis testing. A simple version of this problem in machine learning is known as overfitting, but the same problem can arise at different phases of the process and thus a train/test split – when applicable at all – may not be sufficient to prevent this from happening.[17]

The final step of knowledge 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 data mining algorithms are necessarily valid. It is common for the 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 set of data on 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” 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.

Research

The premier professional body in the field is the Association for Computing Machinery‘s (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD).[18][19] Since 1989 this ACM SIG has hosted an annual international conference and published its proceedings,[20] and since 1999 it has published a biannual academic journal titled “SIGKDD Explorations”.[21]

Computer science conferences on data mining include:

Data mining topics are also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases

Standards

There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.

For exchanging the extracted models – in particular for use in predictive analytics – the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. 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.[22]

Notable uses

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.

Privacy concerns and ethics

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).[23]

The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics.[24] In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.[25][26]

Data mining requires data preparation which can uncover information or patterns which may 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 identification of private, individual-level data deducible or otherwise apparent).[27] This is not data mining per se, but a result of the preparation of data before – and for the purposes of – the analysis. 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.[28][29][30]

It is recommended that an individual is made aware of the following before data are collected:[27]

  • the purpose of the data collection and any (known) data mining projects;
  • how the data will be used;
  • who will be able to mine the data and use the data and their derivatives;
  • the status of security surrounding access to the data;
  • how collected data can be updated.

Data may also be modified so as to become anonymous, so that individuals may not readily be identified.[27] However, even “de-identified”/”anonymized” data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.[31]

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 2011 for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies.[32]

Situation in Europe

Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, the U.S.-E.U. Safe Harbor Principles currently effectively expose European users to privacy exploitation by U.S. companies. 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.[citation needed]

Situation in the United States

In the United States, privacy concerns have been addressed by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA). The HIPAA requires individuals to give their “informed consent” regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week, “‘[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,’ says the AAHC. More importantly, the rule’s goal of protection through informed consent is undermined by the complexity of consent forms that are required of patients and participants, which approach a level of incomprehensibility to average individuals.”[33] This underscores the necessity for data anonymity in data aggregation and mining practices.

U.S. information privacy legislation such as HIPAA and the Family Educational Rights and Privacy Act (FERPA) applies only to the specific areas that each such law addresses. Use of data mining by the majority of businesses in the U.S. is not controlled by any legislation.

Copyright Law

Situation in Europe

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 2014[34] 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 2009 for data mining. 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 2013, under the title of Licences for Europe.[35] The focus on the solution to this legal issue being licences and not limitations and exceptions led to representatives of universities, researchers, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.[36]

Situation in the United States

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 digitisation project displayed – one being text and data mining.[37]

Software

Free open-source data mining software and applications

The following applications are available under free/open source licenses. Public access to application sourcecode is also available.

  • Carrot2: Text and search results clustering framework.
  • Chemicalize.org: A chemical structure miner and web search engine.
  • ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.
  • GATE: a natural language processing and language engineering tool.
  • KNIME: The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.
  • Massive Online Analysis (MOA): a real-time big data stream mining with concept drift tool in the Java programming language.
  • ML-Flex: A software package that enables users to integrate with third-party machine-learning packages written in any programming language, execute classification analyses in parallel across multiple computing nodes, and produce HTML reports of classification results.
  • MLPACK library: a collection of ready-to-use machine learning algorithms written in the C++ language.
  • MEPX – cross platform tool for regression and classification problems based on a Genetic Programming variant.
  • NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.
  • OpenNN: Open neural networks library.
  • Orange: A component-based data mining and machine learning software suite written in the Python language.
  • R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.
  • scikit-learn is an open source machine learning library for the Python programming language
  • Torch: An open sourcedeep learning library for the Lua programming language and scientific computing framework with wide support for machine learning algorithms.
  • UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.
  • Weka: A suite of machine learning software applications written in the Java programming language.

Proprietary data-mining software and applications

The following applications are available under proprietary licenses.

Marketplace surveys

Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. Some of these reports include:

  • Hurwitz Victory Index: Report for Advanced Analytics as a market research assessment tool, it highlights both the diverse uses for advanced analytics technology and the vendors who make those applications possible.Recent-research
  • 2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery[38]
  • Rexer Analytics Data Miner Surveys (2007–2015)[39]
  • Forrester Research 2010 Predictive Analytics and Data Mining Solutions report[40]
  • Gartner 2008 “Magic Quadrant” report[41]
  • Robert A. Nisbet’s 2006 Three Part Series of articles “Data Mining Tools: Which One is Best For CRM?”[42]
  • Haughton et al.’s 2003 Review of Data Mining Software Packages in The American Statistician[43]
  • Goebel & Gruenwald 1999 “A Survey of Data Mining a Knowledge Discovery Software Tools” in SIGKDD Explorations[44]

See also

Methods
Application domains
Application examples
Related topics

Data mining is about analyzing data; for information about extracting information out of data, see:

References

Story 4: U.S. Economic Growth Stagnating — Videos

Real GDP: Percent Change from Preceding Quarter

Image result for U.S. real Gross Domestic Product by quarter

 

Image result for U.S. real Gross Domestic Product by quarter

Image result for U.S. real Gross Domestic Product by quarter

U.S. Economic Growth Slows To .7% In First Quarter

[ Eric Sprott ] — 28 April 2017 — The stalling U S economy

Ep. 245: U.S. GDP Cools As Eurozone Inflation Heats Up

We Are Dangerously Close to a Recession

Rickards: Predict the Unpredictable… We’re Heading Straight Into a Recession

Business Cycles Explained: Austrian Theory

Business Cycles Explained: Keynesian Theory

Is Economic Stagnation Our Future?

What is Secular Stagnation

Investigating ‘Secular Stagnation’

Overdose: The Next Financial Crisis | Full Documentary

The Men Who Crashed the World – Meltdown Part 1

The Men Who Crashed the World – Meltdown Part 2

The Men Who Crashed the World – Meltdown Part 3

The Men Who Crashed the World – Meldown Part 4

[ Marc Faber ] — 26 April 2017 — The biggest risk to markets right now

World’s Greatest Memory and Trump’s La la Land | David Stockman’s Warning

[ David Stockman] — 27 April 2017 — Trump’s tax plan will be dead before arrival

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1929 The Great Depression Par

The Great Depression: Crash Course US History #33

Hoover and the Great Depression

Published on Jun 16, 2014

A new history of the Great Depression is emerging. One that acknowledges the role that government played in causing and prolonging it, and the constructive role that free enterprise could have played, if it were given the chance. In this video, UCLA economist Lee Ohanian explains how Herbert Hoover, widely misunderstood as a champion of the free market, actually turned what should have just been a recession into a depression due to his mistrust of the market.

Milton Friedman – The Great Depression Myth

t 1

1929 The Great Depression Part 2

The Great Depression 3 – New Deal, New York

The Great Depression 4 – We have a plan

The Great Depression 5 – Mean things happening

The Great Depression 6 – To be somebody

The Great Depression 7 – Arsenal of democracy

 

MBARGOED UNTIL RELEASE AT 8:30 A.M. EDT, Friday, April 28, 2017
BEA 17—19

* See the navigation bar at the right side of the news release text for links to data tables, contact personnel and their telephone numbers, and supplementary materials.

Lisa S. Mataloni: (301) 278-9083 (GDP) gdpniwd@bea.gov
Jeannine Aversa: (301) 278-9003 (News Media) Jeannine.aversa@bea.gov

National Income and Product Accounts
Gross Domestic Product: First Quarter 2017 (Advance Estimate)

Real gross domestic product (GDP) increased at an annual rate of 0.7 percent in the first quarter of 2017
(table 1), according to the "advance" estimate released by the Bureau of Economic Analysis. In the
fourth quarter of 2016, real GDP increased 2.1 percent.

The Bureau emphasized that the first-quarter advance estimate released today is based on source data
that are incomplete or subject to further revision by the source agency (see “Source Data for the
Advance Estimate” on page 2). The "second" estimate for the first quarter, based on more complete
data, will be released on May 26, 2017.

Real GDP: Percent Change from Preceding Quarter
The increase in real GDP in the first quarter reflected positive contributions from nonresidential fixed
investment, exports, residential fixed investment, and personal consumption expenditures (PCE), that
were offset by negative contributions from private inventory investment, state and local government
spending, and federal government spending. Imports, which are a subtraction in the calculation of GDP,
increased (table 2).

The deceleration in real GDP in the first quarter reflected a deceleration in PCE and downturns in private
inventory investment and in state and local government spending that were partly offset by an upturn in
exports and accelerations in both nonresidential and residential fixed investment.


_box

Upcoming Annual Update of the National Income and Product Accounts

The annual update of the national income and product accounts, covering the first quarter of 2014
through the first quarter of 2017, will be released along with the "advance" estimate of GDP for the
second quarter of 2017 on July 28.  For more information, see the Technical Note.
___

Current-dollar GDP increased 3.0 percent, or $137.9 billion, in the first quarter to a level of $19,007.3
billion. In the fourth quarter, current-dollar GDP increased 4.2 percent, or $194.1 billion (table 1 and
table 3).

The price index for gross domestic purchases increased 2.6 percent in the first quarter, compared with
an increase of 2.0 percent in the fourth quarter (table 4). The PCE price index increased 2.4 percent,
compared with an increase of 2.0 percent. Excluding food and energy prices, the PCE price index
increased 2.0 percent, compared with an increase of 1.3 percent (appendix table A).


Personal Income (table 10)

Current-dollar personal income increased $161.9 billion in the first quarter, compared with an increase
of $154.6 billion in the fourth. The acceleration in personal income primarily reflected an acceleration in
government social benefits to persons that was partly offset by a downturn in personal dividend income.

Disposable personal income increased $121.0 billion, or 3.4 percent, in the first quarter, compared with
an increase of $141.6 billion, or 4.1 percent, in the fourth. Real disposable personal income increased
1.0 percent, compared with an increase of 2.0 percent.

Personal saving was $814.2 billion in the first quarter, compared with $778.9 billion in the fourth. The
personal saving rate -- personal saving as a percentage of disposable personal income -- was 5.7 percent
in the first quarter, compared with 5.5 percent in the fourth.


Source Data for the Advance Estimate

       Information on the assumptions used for unavailable source data in the advance estimate is
provided in a Technical Note that is posted with the news release on BEA’s Web site. Within a few days
after the release, a detailed "Key Source Data and Assumptions" file is posted on the Web site. For
information on updates to GDP, see the "Additional Information" section that follows.

                                       *          *          *

                            Next release:  May 26, 2017 at 8:30 A.M. EDT
                    Gross Domestic Product:  First Quarter 2017 (Second Estimate)
                    Corporate Profits:  First Quarter 2017 (Preliminary Estimate)

                                       *          *          *



                                      Additional Information

Resources

Additional Resources available at www.bea.gov:
•	Stay informed about BEA developments by reading the BEA blog, signing up for BEA’s email
        subscription service, or following BEA on Twitter @BEA_News.
•	Historical time series for these estimates can be accessed in BEA’s Interactive Data Application.
•	Access BEA data by registering for BEA’s Data Application Programming Interface (API).
•	For more on BEA’s statistics, see our monthly online journal, the Survey of Current Business.
•	BEA's news release scheduleNIPA Handbook:  Concepts and Methods of the U.S. National Income and Product Accounts

Definitions

Gross domestic product (GDP) is the value of the goods and services produced by the nation’s economy
less the value of the goods and services used up in production. GDP is also equal to the sum of personal
consumption expenditures, gross private domestic investment, net exports of goods and services, and
government consumption expenditures and gross investment.

Current-dollar estimates are valued in the prices of the period when the transactions occurred—that is,
at “market value.” Also referred to as “nominal estimates” or as “current-price estimates.”
Real values are inflation-adjusted estimates—that is, estimates that exclude the effects of price changes.
The gross domestic purchases price index measures the prices of final goods and services purchased by
U.S. residents.

The personal consumption expenditure price index measures the prices paid for the goods and services
purchased by, or on the behalf of, “persons.”

Personal income is the income received by, or on behalf of, all persons from all sources:  from
participation as laborers in production, from owning a home or business, from the ownership of
financial assets, and from government and business in the form of transfers. It includes income from
domestic sources as well as the rest of world. It does not include realized or unrealized capital gains or
losses.

Disposable personal income is the income available to persons for spending or saving. It is equal to
personal income less personal current taxes.

Personal outlays is the sum of personal consumption expenditures, personal interest payments, and
personal current transfer payments.

Personal saving is personal income less personal outlays and personal current taxes.
The personal saving rate is personal saving as a percentage of disposable personal income. (For a
comparison of personal saving in BEA's national income and product accounts (NIPAs) with personal
saving in the Federal Reserve Board's financial accounts of the United States, go to
www.bea.gov/national/nipaweb/nipa-frb.asp.

For more definitions, see the Glossary: National Income and Product Accounts.


Statistical conventions

Annual rates. Quarterly values are expressed at seasonally-adjusted annual rates (SAAR), unless
otherwise specified. Dollar changes are calculated as the difference between these SAAR values. For
detail, see the FAQ “Why does BEA publish estimates at annual rates?”

Percent changes in quarterly series are calculated from unrounded data and are displayed at annual
rates, unless otherwise specified. For details, see the FAQ “How is average annual growth calculated?”

Quantities and prices. Quantities, or “real” volume measures, and prices are expressed as index
numbers with a specified reference year equal to 100 (currently 2009). Quantity and price indexes are
calculated using a Fisher-chained weighted formula that incorporates weights from two adjacent
periods (quarters for quarterly data and annuals for annual data). “Real” dollar series are calculated by
multiplying the published quantity index by the current dollar value in the reference year (2009) and
then dividing by 100. Percent changes calculated from real quantity indexes and chained-dollar levels
are conceptually the same; any differences are due to rounding.

Chained-dollar values are not additive because the relative weights for a given period differ from those
of the reference year. In tables that display chained-dollar values, a “residual” line shows the difference
between the sum of detailed chained-dollar series and its corresponding aggregate.


Updates to GDP

BEA releases three vintages of the current quarterly estimate for GDP:  "Advance" estimates are
released near the end of the first month following the end of the quarter and are based on source data
that are incomplete or subject to further revision by the source agency; “second” and “third” estimates
are released near the end of the second and third months, respectively, and are based on more detailed
and more comprehensive data as they become available.

Annual and comprehensive updates are typically released in late July. Annual updates generally cover at
least the 3 most recent calendar years (and their associated quarters) and incorporate newly available
major annual source data as well as some changes in methods and definitions to improve the accounts.
Comprehensive (or benchmark) updates are carried out at about 5-year intervals and incorporate major
periodic source data, as well as major conceptual improvements.
The table below shows the average revisions to the quarterly percent changes in real GDP between
different estimate vintages, without regard to sign.

Vintage                               Average Revision Without Regard to Sign
                                         (percentage points, annual rates)
Advance to second                                     0.5
Advance to third                                      0.6
Second to third                                       0.2
Advance to latest                                     1.1
Note - Based on estimates from 1993 through 2015. For more information on GDP updates, see Revision
Information on the BEA Web site.

The larger average revision from the advance to the latest estimate reflects the fact that periodic
comprehensive updates include major statistical and methodological improvements.

Unlike GDP, an advance current quarterly estimate of GDI is not released because data on domestic
profits and on net interest of domestic industries are not available. For fourth quarter estimates, these
data are not available until the third estimate.
https://www.bea.gov/newsreleases/national/gdp/gdpnewsrelease.htm

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