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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets ("big data") involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
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. 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.
The term is a misnomer because the goal is the extraction of patterns and knowledge from large, amount, of data, not the extraction of data itself. It also is a buzzword 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 popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was the Originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing; reasons. Often the more general terms "(large scale) data analysis", or "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). 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.
- Research and evolution
- Data mining
- Results validation
- Notable uses
- Privacy concerns and ethics
- Situation in Europe
- Situation in the United States
- Copyright Law
- Situation in Europe
- Situation in the United States
- Free open-source data mining software and applications
- Commercial data-mining software and applications
- Marketplace surveys
--- See also
- Further reading
- External links
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 apriori hypothesis. The term "Data Mining" appeared around 1990 in the database community. For a short time in 1980's, a phrase "database mining"™, was used, but since it was trademarked by HNC, a
San Diego-based company, to pitch their Database Mining Workstation; 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. Currently, Data Mining and Knowledge Discovery are used interchangeably. Since about 2007, "Predictive Analytics" and since 2011, "Data Science" terms were also used to describe this field.
The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem 1700's, and regression analysis 1800's. 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 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.
Research and evolution ==>
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). Since 1989, this ACM SIG has hosted an annual international conference and published its proceedings, and since 1999 it has published a biannual academic journal titled "SIGKDD Explorations".
==> Computer science conferences on data mining include:
CIKM Conference – ACM Conference on Information and Knowledge Management
DMIN Conference – International Conference on Data Mining
DMKD Conference – Research Issues on Data Mining and Knowledge Discovery
ECDM Conference – European Conference on Data Mining
ECML-PKDD Conference – European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
EDM Conference – International Conference on Educational Data Mining
INFOCOM Conference – IEEE INFOCOM
ICDM Conference – IEEE International Conference on Data Mining
KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining
MLDM Conference – Machine Learning and Data Mining in Pattern Recognition
PAKDD Conference – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining
PAW Conference – Predictive Analytics World
SDM Conference – SIAM International Conference on Data Mining (SIAM)
SSTD Symposium – Symposium on Spatial and Temporal Databases
WSDM Conference – ACM Conference on Web Search and Data Mining
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
The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:
(4) Data Mining
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
==> or a simplified process such as
(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.
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, and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.
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:
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 modeling) – 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 ==>
Data mining can unintentionally be misused, and can then produce results which appear to be significant; but which do not actually predict future behavior 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.
Wiki letter w.svg
This section is missing information about non-classification tasks in data mining. It only covers machine learning. Please expand the section to include this information. Further details may exist on the talk page. (September 2011)
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.
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.
Notable uses ==>
Main article: Examples of data mining
==> See also: Category: Applied data mining.
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 has 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. 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.
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 deductible or otherwise apparent).
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.
It is recommended that an individual is made aware of the following before data are collected:
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. 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.
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.
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." 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.
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 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. The focus on the solution to this legal issue being licenced 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.
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 digitization 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
==> Category: Data mining and machine learning software.
Free open-source data mining software and applications
Carrot2: Text and search results clustering framework.
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 analyzes 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.
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.
SCaViS: Java cross-platform data analysis framework developed at Argonne National Laboratory.
SenticNet API: A semantic and effective resource for opinion mining and sentiment analysis.
Tanagra: A visualisation-oriented data mining software, also for teaching.
Torch: An open source deep 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.
==> Commercial data-mining software and applications
Angoss KnowledgeSTUDIO: data mining tool provided by Angoss.
Clarabridge: enterprise class text analytics solution.
Grapheme: data mining and visualization software provided by iChrome.
HP Vertica Analytics Platform: data mining software provided by HP.
IBM SPSS Modeler: data mining software provided by IBM.
KXEN Modeler: data mining tool provided by KXEN.
LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent OptimizatioN (LION) approach.
Microsoft Analysis Services: data mining software provided by Microsoft.
NetOwl: suite, of multilingual text and entity analytics products that enable data mining.
OpenText™ Big Data Analytics: Visual Data Mining & Predictive Analysis by Open Text Corporation
Oracle Data Mining: data mining software by Oracle.
PSeven: platforms for automation of engineering simulation and analysis, multidisciplinary optimization and data mining provided by DATADVANCE.
Qlucore Omics Explorer: data mining software provided by Qlucore.
RapidMiner: An environment for machine learning and data mining experiments.
SAS Enterprise Miner: data mining software provided by the SAS Institute.
STATISTICA Data Miner: data mining software provided by StatSoft.
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:
2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Rexer Analytics Data Miner Surveys (2007–2013)
Forrester Research 2010 Predictive Analytics and Data Mining Solutions report
Gartner 2008 "Magic Quadrant" report
Robert A. Nisbet's 2006 Three Part Series of articles "Data Mining Tools: Which One is Best For CRM?"
Haughton et al.'s 2003 Review of Data Mining Software Packages in The American Statistician
Goebel & Gruenwald 1999 "A Survey of Data Mining a Knowledge Discovery Software Tools" in SIGKDD Explorations
Association rule learning
Multilinear subspace learning
Structured data analysis
Support vector machines
Decision support system
Exploratory data analysis
Category: Applied data mining.
Data mining in agriculture
Data mining in meteorology
Educational data mining
National Security Agency
Police-enforced ANPR in the UK
Quantitative structure–activity relationship
Surveillance / Mass surveillance (e.g., Stellar Wind)
==> Data mining is about analyzing data; for information about extracting information out of data,
Profiling (information science)
Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). "From Data Mining to Knowledge Discovery in Databases" (PDF). Retrieved 17 December 2008.
Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Retrieved 2010-12-09.
Jump up ^ Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Retrieved 2012-08-07.
Han, Jiawei; Kamber, Micheline (2001). Data mining: concepts and techniques. Morgan Kaufmann. p. 5. ISBN 9781558604896. Thus, data mining should have been more appropriately named "knowledge mining from data," which is unfortunately somewhat long
See e.g. OKAIRP 2005 Fall Conference, Arizona State University About.com: Data mining
Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011). Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier. ISBN 978-0-12-374856-0.
Bouckaert, Remco R.; Frank, Eibe; Hall, Mark A.; Holmes, Geoffrey; Pfahringer, Bernhard; Reutemann, Peter; Witten, Ian H. (2010). "WEKA Experiences with a Java open-source project". Journal of Machine Learning Research 11: 2533–2541. the original title, "Practical machine learning", was changed ... The term "data mining" was [added] primarily for marketing reasons.
Mena, (2011). Machine Learning Forensics for Law Enforcement, Security, and Intelligence. Boca Raton, FL: CRC Press (Taylor & Francis Group). ISBN 978-1-4398-6069-4.
Piatetsky-Shapiro, Gregory; Parker, Gary (2011). "Lesson: Data Mining, and Knowledge Discovery: An Introduction".
Introduction to Data Mining. KD Nuggets. Retrieved 30 August 2012.
Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. ISBN 0-471-22852-4. OCLC 50055336.
"Microsoft Academic Search: Top conferences in data mining". Microsoft Academic Search.
"Google Scholar: Top publications - Data Mining & Analysis". Google Scholar.
Proceedings, International Conferences on Knowledge Discovery and Data Mining, ACM, New York.
Óscar Marbán, Gonzalo Mariscal and Javier Segovia (2009); A Data Mining & Knowledge Discovery Process Model. In Data Mining and Knowledge Discovery in Real Life Applications, Book edited by Julio Ponce and Adem Karahoca, ISBN 978-3-902613-53-0, pp. 438–453, February 2009, I-Tech, Vienna, Austria.
Lukasz Kurgan and Petr Musilek (2006); A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review. Volume 21 Issue 1, March 2006, pp 1–24, Cambridge University Press, New York, NY, USA doi:10.1017/S0269888906000737
Azevedo, A. and Santos, M. F. KDD, SEMMA and CRISP-DM: a parallel overview. In Proceedings of the IADIS European Conference on Data Mining 2008, pp 182–185.
Günnemann, Stephan; Kremer, Hardy; Seidl, Thomas (2011). "An extension of the PMML standard to subspace clustering models". Proceedings of the 2011 workshop on Predictive markup language modeling - PMML '11. p. 48. doi:10.1145/2023598.2023605. ISBN 9781450308373.
Seltzer, William. "The Promise and Pitfalls of Data Mining: Ethical Issues" (PDF).
Pitts, Chip (15 March 2007). "The End of Illegal Domestic Spying? Don't Count on It". Washington Spectator.
Taipale, Kim A. (15 December 2003). "Data Mining and Domestic Security: Connecting the Dots to Make Sense of Data". Columbia Science and Technology Law Review 5 (2). OCLC 45263753. SSRN 546782.
Resig, John; and Teredesai, Ankur (2004). "A Framework for Mining Instant Messaging Services". Proceedings of a 2004,
SIAM DM Conference.
Think Before You Dig: Privacy Implications of Data Mining & Aggregation, NASCIO Research Brief, September 2004
Ohm, Paul. "Don't Build a Database of Ruin". Harvard Business Review.
Darwin Bond-Graham, Iron Casebook - The Logical End of Facebook's Patents, Counterpunch.org, 2013.12.03
Darwin Bond-Graham, Inside the Tech industry’s Startup Conference, Counterpunch.org, 2013.09.11
AOL search data identified individuals, SecurityFocus, August 2006
Biotech Business Week Editors (June 30, 2008); BIOMEDICINE; HIPAA Privacy Rule Impedes Biomedical Research, Biotech Business Week, retrieved 17 November 2009 from LexisNexis Academic
UK Researchers Given Data Mining Right Under New UK Copyright Laws. Out-Law.com. Retrieved 14 November 2014
"Licenses for Europe - Structured Stakeholder Dialogue 2013". European Commission. Retrieved 14 November 2014.
"Text and Data Mining: Its importance and the need for change in Europe". Association of European Research Libraries. Retrieved 14 November 2014.
"Judge grants summary judgment in favor of Google Books — a fair use victory". Lexology.com. Antonelli Law Ltd. Retrieved 14 November 2014.
Mikut, Ralf; Reischl, Markus (September–October 2011). "Data Mining Tools". Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1 (5): 431–445. doi:10.1002/widm.24. Retrieved October 21, 2011.
Karl Rexer, Heather Allen, & Paul Gearan (2011); Understanding Data Miners, Analytics Magazine, May/June 2011 (INFORMS: Institute for Operations Research and the Management Sciences).
Kobielus, James; The Forrester Wave: Predictive Analytics and Data Mining Solutions, Q1 2010, Forrester Research, 1 July 2008
Herschel, Gareth; Magic Quadrant for Customer Data-Mining Applications, Gartner Inc., 1 July 2008
Nisbet, Robert A. (2006); Data Mining Tools: Which One is Best for CRM? Part 1, Information Management Special Reports, January 2006
Haughton, Dominique; Deichmann, Joel; Eshghi, Abdolreza; Sayek, Selin; Teebagy, Nicholas; and Topi, Heikki (2003); A Review of Software Packages for Data Mining, The American Statistician, Vol. 57, No. 4, pp. 290–309
Goebel, Michael; Gruenwald, Le (1999); A Survey of Data Mining and Knowledge Discovery Software Tools, SIGKDD Explorations, Vol. 1, Issue 1, pp. 20–33
Further reading ==>
Cabena, Peter; Hadjnian, Pablo; Stadler, Rolf; Verhees, Jaap; Zanasi, Alessandro (1997); Discovering Data Mining: From Concept to Implementation, Prentice Hall, ISBN 0-13-743980-6
M.S. Chen, J. Han, P.S. Yu (1996) "Data mining: an overview from a database perspective". Knowledge and data Engineering, IEEE Transactions on 8 (6), 866-883
Feldman, Ronen; Sanger, James (2007); The Text Mining Handbook, Cambridge University Press, ISBN 978-0-521-83657-9
Guo, Yike; and Grossman, Robert (editors) (1999); High-Performance Data Mining: Scaling Algorithms, Applications, and Systems, Kluwer Academic Publishers
Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. Morgan Kaufmann, 2006.
Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome (2001); The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, ISBN 0-387-95284-5
Liu, Bing (2007); Web Data Mining: Exploring Hyperlinks, Contents and Usage Data, Springer, ISBN 3-540-37881-2
Murphy, Chris (16 May 2011). "Is Data Mining Free Speech?". InformationWeek (UMB): 12.
Nisbet, Robert; Elder, John; Miner, Gary (2009); Handbook of Statistical Analysis & Data Mining Applications, Academic Press/Elsevier, ISBN 978-0-12-374765-5
Poncelet, Pascal; Masseglia, Florent; and Teisseire, Maguelonne (editors) (October 2007); "Data Mining Patterns: New Methods and Applications", Information Science Reference, ISBN 978-1-59904-162-9
Tan, Pang-Ning; Steinbach, Michael; and Kumar, Vipin (2005); Introduction to Data Mining, ISBN 0-321-32136-7
Theodoridis, Sergios; and Koutroumbas, Konstantinos (2009); Pattern Recognition, 4th Edition, Academic Press, ISBN 978-1-59749-272-0
Weiss, Sholom M.; and Indurkhya, Nitin (1998); Predictive Data Mining, Morgan Kaufmann
Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011). Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier. ISBN 978-0-12-374856-0. (See also Free Weka software)
Ye, Nong (2003); The Handbook of Data Mining, Mahwah, NJ: Lawrence Erlbaum
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From Wikipedia, the free encyclopedia
Data Mining Vs Artificial Intelligence Vs Machine Learning - Upfront Analytics
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Data Mining Services
Data Mining Services is the process of extracting patterns from information. Data mining is becoming an increasingly important tool to transform the data into information. Any format including MS excels, CSV, HTML and many such formats according to your requirements.
A Web spider is a computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. Many sites, in particular, search engines, use spidering as a means of providing up-to-date data.
Web grabber is just another name of the data scraping or data extraction.
Web Bot is software, programs that claimed to be able to predict future events by tracking keywords entered on the Internet. Web bot software is the best program to pull out articles, blog, relevant website content and many such website related data We have worked with many clients for data extracting, data scraping and data mining they are really happy with our services we provide very quality services and make your work data work very easy and automatic.
Top 5 Ways Organizations can benefit from Data Mining - Colocation America
Data mining is a computational process used to discover patterns in large data sets. How companies can benefit: All commercial, government, private and even Non-governmental organizations employ the use of both digital and physical data to drive their business processes. Data mining is widely used to gather knowledge in all industries.
Top 5 Ways Organizations can benefit from Data Mining
Data mining is a computational process used to discover patterns in large data sets. How companies can benefit-
All commercial, government, private and even Non-governmental organizations employ the use of both digital and physical data to drive their business processes.the benefits of data mining
Data mining is widely used to gather knowledge in all industries. For those unfamiliar with the concept, a definition of the different types of data mining – along with the benefits to all organizations, is in order.
The most comprehensive definition of this term is "an interdisciplinary, subfield of computer science, data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems."
In laymen terms, data mining is the use of computers and specific software applications to arrange data, process it and provide a usable data output or information as specified by the user.
Where can Data be Sourced from?
As stated above, data can be mined or found through the use of digital tools as well as physical methods from different sources. There are numerous sources a data mining company gets its raw data from across both the digital and physical world and these sources include the following:
1.) Website Data Mining: When launching a new product or services online, the need to conduct a market research usually drives the need for mining websites for relevant data to simplify your research effort. Therefore, mining data from websites simply means the use of research tools to get relevant data from websites, e-commerce stores, online journals etc.
2.) Social Media Data Mining: Today’s most popular means of communication is also one of the most important sources available for researchers. Data mining companies use social media to get the required information to drive their market/organizational analysis.
3.) Business Data Mining: Competing businesses and organizations get data from company records on their working personnel or business processes for use in their own business research. Excel or Document Mining: When searching for data on the web, focus should be placed on getting relevant documents—such as Excel, Ms, Word and PDF files—with information or data on the topic of your research.
4.) Resume Information Mining: Resumes of job applicants can tell you a lot about human resources, filling up job applications or posting job opportunities for the general public. Therefore streamlining your mining effort to include resume research is a sure way of getting more balanced information about a particular subject.
5.) Music Mining: Other sources for dredging up data such as music mining, visual mining, and sensor mining also exist and are used for more specific organizational needs
The benefits of Data Mining
Lederhosen, -pickax-murderer, The benefits of mining data covers almost all facets of life which include; gaming, policing, business, science, engineering, human rights organizations and surveillance.
When dealing with a plethora of data sources, confusion usually arises for the incompetent researcher who has neither the tools nor experience to handle bulky projects. Outsourcing data mining projects reduces the risk of dredging up ineffective data.
Data mining helps organizations get the necessary information needed to handle different processes as quickly as possible. For the surveillance and policing industries, the need to meet deadlines and process information quickly is of the utmost importance.
Outsourcing bulky data mining projects ultimately helps an organization manage its human resources, its capital expenditures and focus on core business areas.
Big data still means big data. If you know what big data means and how to use it—good for you. If you don’t, then you’re in the same boat as many of us.
Big data has slowly become a buzzword—something that gets said repeatedly but has an ambiguous meaning.
On the surface, big data means storing large amounts of data and playing connect the dots to glean a trend through a specific algorithm. It’s called “big data” because there’s too much information for human beings to sift through. It’s like trying to find the grain of salt on the beach.
But What Does Big Data Really Mean?
Our friends at Wikipedia can bore you for ages about the topic, and our more focused friends at arsTechnica can give you a more reader-friendly explanation, but ours is simple: Big data is what computers were designed for in the first place.
Throw away the endless files and papers and store all the information on a machine where that machine can analyze the information while you do something else.
A great example of how big data is used is a basic Google search. Imagine typing an inquiry and waiting for some person at Google to sift through endless pages of the internet until he/she gets back to you with your answer a good week or so later. Instead, the answer is instantaneous because Google’s algorithms have already done all the work. Brilliant!
How Can You Use Big Data?
But on a smaller scale, let’s say your business, how is big data applicable. Your business most likely does not have the resources that Google has. Instead, maybe you own a law firm and you want to analyze which stipulation in a lawsuit ends well or worse during Trials; That would give you a better idea of whether or not to take (and win) the case.
You have all this data lying around, it’s up to you to sort through all the variables. Or rather, it’s up to you to find the correct software to do that for you. Maybe take a dedicated server (and you’ll need one with all the data you’ll be analyzing) and store all the “big data” on there while your software does the work for you. That’s it. There’s plenty of software out there to suit your specific needs.
Now get out there and use big data to get big results.
Big Data – two words that get tossed around quite feverishly yet there’s only a small group of people who actually know how to harness it, the right way.
Over the last couple of years, you may have heard the words “Big Data” pop up in feverish boardroom meetings or a variety of tech-savvy publications. However, there is only a handful of people in the industry who have recognized the underlying potential and implications of big data as a business tool.
Big data is simply the combination of structured and unstructured data aggregated by a company. It has no inherent business value of its own, and its true value is only created after extensive evaluation and analysis.
The real value of big data lies in your ability to zero down on the information that will give you essential consumer insights regarding your products. This will allow you to optimize your organization’s business strategies in order to target consumer needs more accurately.
What Drives the Consumer Life Cycle?
The innovation of big data is a key facilitator of the new marketing paradigm that encourages organizations to abandon conventional, monotonous marketing models in favor of customized and strategic plans. Big data allows you to harness essential results across the whole spectrum of the standard customer life cycle, which allows them to understand their success and shortcomings in each field.
The objective of every marketing model is to achieve brand ubiquity across all the organizational channels utilized to market your products. Big data essentially gives you the most accurate insights into the mindset of your consumers that will help you accordingly tweak your strategies to target your user base perfectly and attract a lot more customers.;
Even if you own a company marketing something highly technical and mundane like GPS heavy machinery, you can still devise a creative engagement plan to captivate the attention of your target audience.
Harnessing Customer Intelligence
Consumer intelligence is what we classify as the data aggregated by your company after a phase of customer interaction. Each campaign launched by your organization generates a massive quantity of data.
However, the quality of data is what really matters at the end of the day. The true value of customer intelligence is determined by the depth of the insights it gives you for promoting your brand during every possible stage of your marketing campaign.
This helps you draw out better marketing strategies that target and serve each segment of your user base and ensure all their needs are adequately met with.
Consumer Life Cycle
The consumer life cycle is the driving force of any big data engine, and in turn, the power of the big data engine is what helps your company’s marketing campaign hit a home run. Hence, it is important to prioritize the right data to extract and analyze rather than be drowned in a sea of irrelevant information.
A well-implemented data engine can help you build the perfect bridge of harmony that you always dreamed of between your customers and your organization.
Aaron Walker is a proud dad and gadget enthusiast. He started blogging about a year ago but has been writing freelance for over a decade. See more of his work on Twitter at @aaronwalker77.
Editor’s Note: The original article (posted below the line) was published on August 06, 2013. An update on the subject matter….
It’s been a good two years since we updated our readers on big data, so let’s see what’s happened since then.
Drilling deep to find Big Data riches
Logistics has long been much more than simply handling and transporting goods. Information plays a role and gives businesses a decisive competitive edge. We've all heard the old adage that knowledge is power. Vast amounts of information from diverse sources - aka "big data" - hold a huge treasure trove of hidden potential.