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Data Mining: What is Data Mining?

Data Mining: What is Data Mining?
Overview Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Continuous Innovation Although data mining is a relatively new term, the technology is not. Example For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. Data, Information, and Knowledge Data Data are any facts, numbers, or text that can be processed by a computer. Information Knowledge Data Warehouses What can data mining do? Related:  Data Mining - Text Mining = Fouille de Données, de TextesData mining and other covert collection of Information

An Introduction to Data Mining An Introduction to Data Mining Discovering hidden value in your data warehouse Overview Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Most companies already collect and refine massive quantities of data. This white paper provides an introduction to the basic technologies of data mining. The Foundations of Data Mining Data mining techniques are the result of a long process of research and product development. Massive data collection Powerful multiprocessor computers Data mining algorithms Commercial databases are growing at unprecedented rates. In the evolution from business data to business information, each new step has built upon the previous one. Table 1. The Scope of Data Mining Automated prediction of trends and behaviors. Databases can be larger in both depth and breadth: More columns. How Data Mining Works Conclusion

Are data mining and data warehousing related? - HowStuffWorks Both data mining and data warehousing are business intelligence tools that are used to turn information (or data) into actionable knowledge. The important distinctions between the two tools are the methods and processes each uses to achieve this goal. Data mining is a process of statistical analysis. Analysts use technical tools to query and sort through terabytes of data looking for patterns. Usually, the analyst will develop a hypothesis, such as customers who buy product X usually buy product Y within six months. Data warehousing describes the process of designing how the data is stored in order to improve reporting and analysis. So the crux of the relationship between data mining and data warehousing is that data, properly warehoused, is easier to mine.

Text mining : vers un nouvel accord avec Elsevier | Sciences communes La semaine est placée sous le signe de la divulgation de documents officiels sur le text mining (pourrait-on parler de MiningLeaks ?). Le collectif Savoirscom1 vient de publier le rapport du Conseil supérieur de la propriété littéraire et artistique sur « l’exploration de données ». De mon côté, j’apporte quelques informations sur l’accord conclu entre le consortium Couperin et Elsevier concernant la licence de data et text mining accordée par le géant de l’édition scientifique à plusieurs centaines d’établissements universitaires et hospitaliers français. Contre toute attente, les nouvelles sont meilleures du côté d’Elsevier que du CSPLA : en digne représentant des ayants-droits, le Conseil vient de retoquer toute éventualité d’exception au droit d’auteur pour les projets scientifiques de text mining (alors que le Royaume-Uni vient tout juste d’en voter une, et qu’il s’agit d’un des principaux axes des projets de réforme européens du droit d’auteur). Ce projet initial a été clarifié.

Major issues in data mining Every project should be undertaken with all the necessary preparations. Data mining is no different. This tip from... By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You also agree that your personal information may be transferred and processed in the United States, and that you have read and agree to the Terms of Use and the Privacy Policy. Jiawei Han and Micheline Kamber's book Data Mining: Concepts and Techniques (Morgan Kaufman) provides a list of the major issues involved in data mining. Mining methodology and user interaction issues: These reflect the kinds of knowledge mined, the ability to mine knowledge at multiple granularities, the use of domain knowledge, ad hoc mining, and knowledge visualization. Performance issues: These include efficiency, scalability, and parallelization of data mining algorithms. Issues relating to the diversity of database types: What did you think of this tip?

Data Mining and Statistical Modeling A recurring question and point of debate in the realm of analytics is whether there exists any meaningful difference between data mining and statistics. (Text mining or text analytics is not addressed here, although this area of unstructured or semi-structured data analysis has certain similarities as well as points of integration with data mining, the latter dealing with structured data.) Some regard statistics as referring to hypothesis-driven analysis of smaller data sets, while data mining refers to discovery-driven analysis of large databases. Others view the two terms as simply different names for extracting useful information and deriving conclusions from data. Brieman describes two “cultures” or viewpoints about data analysis, with statisticians assuming that observed data are generated by a given data model while data miners make no assumptions about the data generation mechanism and instead rely on algorithms to search for patterns in usually large and complex data sets.

Data mining Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also 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] Etymology[edit] In the 1960s, 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. Process[edit]

Text mining A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. Text mining and text analytics[edit] The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation.[1] The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining"[2] in 2004 to describe "text analytics The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. History[edit] Text analysis processes[edit] Subtasks — components of a larger text-analytics effort — typically include: Software[edit]

Smith | Intelligence Collection and Covert Action: Time for a Divorce? A retired CIA station chief examines they marriage between human intelligence collection and covert action that came about in the early years of the Cold War and its detrimental effects on the Agency’s ability to produce useful and timely intelligence on U.S. enemies. If we cannot eliminate covert action entirely, he concludes, it should at least be separated from the intelligence collection function. – Ed. America has lived with its “Intelligence Community” – the CIA, NSA, DIA and all the other lesser intelligence organizations – for decades. Depending on your viewpoint, they have been somewhere between successful and unsuccessful in providing our government both with the organizational structure and with the intelligence needed to protect our country and advance its international interests. Whatever your take, there is one immutable involved in intelligence work: It is an aggressive, risk-taking business that withers when bureaucratic inertia and caution settle in.

A quick introduction to R 'R' is a programming language for data analysis and statistics. It is free, and very widely used by professional statisticians. It is also very popular in certain application areas, including bioinformatics. R is a dynamically typed interpreted language, and is typically used interactively. Vectors Vectors are a fundamental concept in R, as many functions operate on and return vectors, so it is best to master these as soon as possible. > rep(1,10) [1] 1 1 1 1 1 1 1 1 1 1 > Here rep is a funtion that returns a vector (here, 1 repeated 10 times). > ? You can assign any object (including vectors) using the assignment operator <-, and combine vectors and scalars with the c function. > a<-rep(1,10) > b<-1:10 > c(a,b) [1] 1 1 1 1 1 1 1 1 1 1 1 2 3 4 5 6 7 8 9 10 > a+b [1] 2 3 4 5 6 7 8 9 10 11 > a+2*b [1] 3 5 7 9 11 13 15 17 19 21 > a/b [1] 1.0000000 0.5000000 0.3333333 0.2500000 0.2000000 0.1666667 0.1428571 [8] 0.1250000 0.1111111 0.1000000 > c(1,2,3) [1] 1 2 3 > > b [1] 1 2 3 4 5 6 7 8 9 10

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