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Orange - Data Mining Fruitful & Fun. Eureqa. Eureqa is a breakthrough technology that uncovers the intrinsic relationships hidden within complex data. Traditional machine learning techniques like neural networks and regression trees are capable tools for prediction, but become impractical when "solving the problem" involves understanding how you arrive at the answer. Eureqa uses a breakthrough machine learning technique called Symbolic Regression to unravel the intrinsic relationships in data and explain them as simple math. Using Symbolic Regression, Eureqa can create incredibly accurate predictions that are easily explained and shared with others. Over 35,000 people have relied on Eureqa to answer their most challenging questions, in industries ranging from Oil & Gas through Life Sciences and Big Box Retail. Eureqa One Page Overview (.pdf) »Visit the Eureqa Community » Eureqa utilizes a machine learning technique called Symbolic Regression to distill raw data into non-linear mathematical equations.

Ckan - The open source data portal software. Weka 3 - Data Mining with Open Source Machine Learning Software in Java. Weka is tried and tested open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j. Download and installDocsCoursesBook. The Top 10 Algorithms in Data Mining. Data-Mining-Algorithms-22.png (Image PNG, 1481x860 pixels) - Redimensionnée (86%) 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 and statistics 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.

Data scraping. Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program. Description[edit] Screen scraping[edit] In the 1980s, financial data providers such as Reuters, Telerate, and Quotron displayed data in 24×80 format intended for a human reader. Users of this data, particularly investment banks, wrote applications to capture and convert this character data as numeric data for inclusion into calculations for trading decisions without re-keying the data. More modern screen scraping techniques include capturing the bitmap data from the screen and running it through an OCR engine, or in the case of GUI applications, querying the graphical controls by programmatically obtaining references to their underlying programming objects.

Web scraping[edit] Report mining[edit] Data Scraping Tools[edit] See also[edit] References[edit] Further reading[edit] Data integration. Data integration involves combining data residing in different sources and providing users with a unified view of these data.[1] This process becomes significant in a variety of situations, which include both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes.[2] It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. In management circles, people frequently refer to data integration as "Enterprise Information Integration" (EII). History[edit] Figure 1: Simple schematic for a data warehouse.

Figure 2: Simple schematic for a data-integration solution. Issues with combining heterogeneous data sources under a single query interface have existed for some time. Example[edit] Theory of data integration[edit] where and . TANAGRA - A free data mining software for research and education. Data Mining. Image: Detail of sliced visualization of thirty video samples of Downfall remixes. See actual visualization below.

As part of my post doctoral research for The Department of Information Science and Media Studies at the University of Bergen, Norway, I am using cultural analytics techniques to analyze YouTube video remixes. My research is done in collaboration with the Software Studies Lab at the University of California, San Diego. A big thank you to CRCA at Calit2 for providing a space for daily work during my stays in San Diego. The following is an excerpt from an upcoming paper titled, “Modular Complexity and Remix: The Collapse of Time and Space into Search,” to be published in the peer review journal AnthroVision, Vol 1.1.

A note will posted here, on Remix Theory, announcing when the complete paper is officially published. The following excerpt references sliced visualizations of the three cases studies in order to analyze the patterns of remixing videos on YouTube. 5 of the Best Free and Open Source Data Mining Software. The process of extracting patterns from data is called data mining. It is recognized as an essential tool by modern business since it is able to convert data into business intelligence thus giving an informational edge. At present, it is widely used in profiling practices, like surveillance, marketing, scientific discovery, and fraud detection. There are four kinds of tasks that are normally involve in Data mining: * Classification - the task of generalizing familiar structure to employ to new data* Clustering - the task of finding groups and structures in the data that are in some way or another the same, without using noted structures in the data.* Association rule learning - Looks for relationships between variables.* Regression - Aims to find a function that models the data with the slightest error.

For those of you who are looking for some data mining tools, here are five of the best open-source data mining software that you could get for free: Orange RapidMiner Weka JHepWork. Exploration de données. Un article de Wikipédia, l'encyclopédie libre. Vous lisez un « bon article ». L'utilisation industrielle ou opérationnelle de ce savoir dans le monde professionnel permet de résoudre des problèmes très divers, allant de la gestion de la relation client à la maintenance préventive, en passant par la détection de fraudes ou encore l'optimisation de sites web. C'est aussi le mode de travail du journalisme de données[1]. L'exploration de données[2] fait suite, dans l'escalade de l'exploitation des données de l'entreprise, à l'informatique décisionnelle.

Histoire[modifier | modifier le code] Collecter les données, les analyser et les présenter au client. De 1919 à 1925, Ronald Fisher met au point l'analyse de la variance comme outil pour son projet d'inférence statistique médicale. L'arrivée progressive des micro-ordinateurs permet de généraliser facilement ces méthodes bayésiennes sans grever les coûts. Applications industrielles[modifier | modifier le code]

Glossaire du data mining. Un article de Wikipédia, l'encyclopédie libre. L'exploration de données étant à l'intersection des domaines de la statistique, de l'intelligence artificielle et de l'informatique, il semble intéressant de faire un glossaire où on peut retrouver les définitions des termes en français et leur équivalent en anglais classées selon ces trois domaines, en indiquant lorsque c'est utile s'il s'agit d'exploration de données "classique", de fouille de texte, du web, de flots de données ou de fichier audio. Informatique[modifier | modifier le code] Dans ce paragraphe est listé le vocabulaire informatique utilisé dans le data mining.

A[modifier | modifier le code] Algorithme (« Algorithm ») : c'est un ensemble d'étapes, d'opérations, de procédures destinées à produire un résultat. C[modifier | modifier le code] F[modifier | modifier le code] M[modifier | modifier le code] Métadonnée (« Metadata ») : Données sur les données. S[modifier | modifier le code] Data Mining[modifier | modifier le code] si on a : où et. Comparatif des logiciels gratuits de Data Mining. Ce site reprend les supports utilisés pour le séminaire du 12 déc 2005 au Laboratoire ERIC. Il s'agissait de déterminer si des logiciels gratuits pouvaient être utilisés dans l'enseignement du Data Mining à l'Université. Le mode de fonctionnement de trois logiciels très répandus dans la communauté de la fouille de données a été décrit en détail : WEKA, ORANGE et TANAGRA.

De mon point de vue, la réponse est double : OUI, si l'objectif est d'expliquer le fonctionnement des méthodes de fouille de données, interpréter les résultats, comparer les techniques ; NON, si l'objectif est de montrer la mise en oeuvre des logiciels de data mining dans les processus industriels. Portail KDNUGGETS » WEKA » ORANGE » TANAGRA » ALPHAMINER » YALE.

The Cooperative Association for Internet Data Analysis. Datatracker - automated web collection. Web Data Extraction, Web Data Mining, Screen Scraping, Email Extractor Services. Data management. Data management comprises all the disciplines related to managing data as a valuable resource. Overview[edit] The official definition provided by DAMA International, the professional organization for those in the data management profession, is: "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise. " {{DAMA International}} This definition is fairly broad and encompasses a number of professions which may not have direct technical contact with lower-level aspects of data management, such as relational database management. Alternatively, the definition provided in the DAMA Data Management Body of Knowledge (DAMA-DMBOK) is: "Data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.

Corporate Data Quality Management[edit] Usage[edit] Category:Data management. From Wikipedia, the free encyclopedia Data management comprises all the disciplines related to managing data as a valuable resource. Subcategories This category has the following 35 subcategories, out of 35 total. Pages in category "Data management" The following 200 pages are in this category, out of 284 total. This list may not reflect recent changes (learn more). (previous 200) (next 200)(previous 200) (next 200) Data Mining - PPDM Wiki. From PPDM Wiki Introduction Traditional data analysis is done by inserting data into standards or customized models. In either case, it is assumed that the relationships among various system variables are well known and can be expressed mathematically.

However, in many cases, relationships may not be known. In such situations, modeling is not possible and a data mining approach may be attempted. Data mining (DM) is a term used to describe knowledge discovery in databases. DM is on the interface of computer science and statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. The major characteristics and objectives of data mining Data are often buried deep within very large databases, which sometimes contain data from several years. Effectively leveraging data mining tools and technologies can lead to acquiring and maintaining a strategic competitive advantage.

How Data Mining Works Classes. Data Exploration. A Programmer's Guide to Data Mining | The Ancient Art of the Numerati. Mozenda Scraper Data Extraction, Web Screen Scraping Tool, Data Mining - Home Page - The Data Mine Wiki. DATA MINING. Data Mining Map. Data Mining Community's Top Resource. Open Data Tools: Turning Data into ‘Actionable Intelligence’ › Scientific and Medical Libraries.

Data Mining, a useful tool in Business Intelligence | Ana María Orozco Zuluaga. In many occasions we have heard about Data Mining but, what is it exactly and when do we have to use it?. Well, I am going to start with some basis definitions I have collected from different sources and authors and I have made a nice combination (from my point of view) that I will share in this post. What is it? Data Mining is an extraction activity and its objective is discovering facts which are in the data base. In the same way it enables you to deduce hidden knowledge by examining or training the data. The knowledge founded is expressed in patterns and rules.

When do we have to use it or when is it useful? Systems partially unknownHuge number of dataPowerful hardware and software Data mining is very useful in many fields such as: Marketing, government, medicine, sales and production. In the figure below I show general information of how each algorithms work, its characteristics and the specifics cases when we use it in a particular case.