Text mining

Algorithm Flow chart of an algorithm (Euclid's algorithm) for calculating the greatest common divisor (g.c.d.) of two numbers a and b in locations named A and B. The algorithm proceeds by successive subtractions in two loops: IF the test B ≥ A yields "yes" (or true) (more accurately the numberb in location B is greater than or equal to the numbera in location A) THEN, the algorithm specifies B ← B − A (meaning the number b − a replaces the old b). Similarly, IF A > B, THEN A ← A − B. The process terminates when (the contents of) B is 0, yielding the g.c.d. in A. In mathematics and computer science, an algorithm ( i/ˈælɡərɪðəm/ AL-gə-ri-dhəm) is a step-by-step procedure for calculations. Informal definition[edit] While there is no generally accepted formal definition of "algorithm," an informal definition could be "a set of rules that precisely defines a sequence of operations Boolos & Jeffrey (1974, 1999) offer an informal meaning of the word in the following quotation: Formalization[edit]

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?

Automatic summarization Methods[edit] Methods of automatic summarization include extraction-based, abstraction-based, maximum entropy-based, and aided summarization. Extraction-based summarization[edit] Two particular types of summarization often addressed in the literature are keyphrase extraction, where the goal is to select individual words or phrases to "tag" a document, and document summarization, where the goal is to select whole sentences to create a short paragraph summary. Abstraction-based summarization[edit] Extraction techniques merely copy the information deemed most important by the system to the summary (for example, key clauses, sentences or paragraphs), while abstraction involves paraphrasing sections of the source document. While some work has been done in abstractive summarization (creating an abstract synopsis like that of a human), the majority of summarization systems are extractive (selecting a subset of sentences to place in a summary). Maximum entropy-based summarization[edit]

Stroop effect Effect of psychological interference on reaction time Green Red BluePurple Red Purple Mouse Top FaceMonkey Top Monkey Naming the font color of a printed word is an easier and quicker task if word meaning and font color are congruent. In psychology, the Stroop effect is the delay in reaction time between congruent and incongruent stimuli. The effect has been used to create a psychological test (the Stroop test) that is widely used in clinical practice and investigation. A basic task that demonstrates this effect occurs when there is a mismatch between the name of a color (e.g., "blue", "green", or "red") and the color it is printed on (i.e., the word "red" printed in blue ink instead of red ink). Original experiment[edit] Stimulus 1: Purple Brown Red Blue Green Stimulus 2: Brown GreenBlueGreen Stimulus 3: ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ Examples of the three stimuli and colors used for each of the activities of the original Stroop article.[1] Experimental findings[edit]

What is Data Mining? A Webopedia Definition Main » TERM » D » By Vangie Beal Data mining requires a class of database applications that look for hidden patterns in a group of data that can be used to predict future behavior. For example, data mining software can help retail companies find customers with common interests. The phrase data mining is commonly misused to describe software that presents data in new ways. Data mining is popular in the science and mathematical fields but also is utilized increasingly by marketers trying to distill useful consumer data from Web sites. Summarize Articles, Editorials and Essays Automatically Competition Competition in sports. A selection of images showing some of the sporting events that are classed as athletics competitions. Consequences[edit] Competition can have both beneficial and detrimental effects. Many evolutionary biologists view inter-species and intra-species competition as the driving force of adaptation, and ultimately of evolution. However, some biologists, most famously Richard Dawkins, prefer to think of evolution in terms of competition between single genes, which have the welfare of the organism 'in mind' only insofar as that welfare furthers their own selfish drives for replication. Biology and ecology[edit] Economics and business[edit] Experts have also questioned the constructiveness of competition in profitability. Three levels of economic competition have been classified: In addition, companies also compete for financing on the capital markets (equity or debt) in order to generate the necessary cash for their operations. Interstate[edit] Law[edit] Politics[edit]

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.

Benchmarking Benchmarking is the process of comparing one's business processes and performance metrics to industry bests or best practices from other industries. Dimensions typically measured are quality, time and cost. In the process of best practice benchmarking, management identifies the best firms in their industry, or in another industry where similar processes exist, and compares the results and processes of those studied (the "targets") to one's own results and processes. Benchmarking is used to measure performance using a specific indicator (cost per unit of measure, productivity per unit of measure, cycle time of x per unit of measure or defects per unit of measure) resulting in a metric of performance that is then compared to others.[1][2] Benefits and use[edit] In 2008, a comprehensive survey[3] on benchmarking was commissioned by The Global Benchmarking Network, a network of benchmarking centers representing 22 countries. Collaborative benchmarking[edit] Procedure[edit] Costs[edit]

List of text mining software From Wikipedia, the free encyclopedia Text mining computer programs are available from many commercial and open source companies and sources. Commercial[edit] Commercial and Research[edit] RxNLP API for Text Mining and NLP – text mining APIs for both research and commercial use. Open source[edit] References[edit] External links[edit]