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Data mining

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Orange - Data Mining Fruitful & Fun. Eureqa. Eureqa is a breakthrough technology that uncovers the intrinsic relationships hidden within complex data.

Eureqa

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. Try Eureqa for yourself - it's free for 30 days.

Eureqa One Page Overview (.pdf) »Visit the Eureqa Community » Ckan - The open source data portal software. Weka 3 - Data Mining with Open Source Machine Learning Software in Java. Weka is a collection of machine learning algorithms for data mining tasks.

Weka 3 - Data Mining with Open Source Machine Learning Software in Java

The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License.

The Top 10 Algorithms in Data Mining. Data-Mining-Algorithms-22.png (Image PNG, 1481x860 pixels) - Redimensionnée (86%) Data mining. 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.[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] Etymology[edit]

Data mining

Data scraping. Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program.

Data scraping

Description[edit] Screen scraping[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

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. The ETL process extracts information from the source databases, transforms it and then loads it into the data warehouse. TANAGRA - A free data mining software for research and education. Data Mining. Image: Detail of sliced visualization of thirty video samples of Downfall remixes.

Data Mining

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. The following excerpt references sliced visualizations of the three cases studies in order to analyze the patterns of remixing videos on YouTube.

Image: this is a slice visualization of “The Charleston and Lindy Hop Dance Remix.” 5 of the Best Free and Open Source Data Mining Software. The process of extracting patterns from data is called data mining.

5 of the Best Free and Open Source Data Mining Software

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. Exploration de données. Un article de Wikipédia, l'encyclopédie libre.

Exploration de données

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. Glossaire du data mining. Un article de Wikipédia, l'encyclopédie libre.

Glossaire du data mining

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. 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. 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.

Data management

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. 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.

(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. Data Exploration.

A Programmer's Guide to Data Mining. 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. 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? 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.