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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 Textes

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

Supports de cours -- Data Mining Cette page recense les supports utilisés pour mes enseignements de Machine Learning, Data Mining et de Data Science au sein du Département Informatique et Statistique (DIS) de l'Université Lyon 2, principalement en Master 2 Statistique et Informatique pour la Science des donnéEs (SISE), formation en data science, dans le cadre du traitement statistique des données et de la valorisation des big data. Je suis très attentif à la synergie forte entre l'informatique et les statistiques dans ce diplôme, ce sont là les piliers essentiels du métier de data scientist. Attention, pour la majorité, il s'agit de « slides » imprimés en PDF, donc très peu formalisés, ils mettent avant tout l'accent sur le fil directeur du domaine étudié et recensent les points importants. Cette page est bien entendu ouverte à tous les statisticiens, data miner et data scientist, étudiants ou pas, de l'Université Lyon 2 ou d'ailleurs. Nous vous remercions par avance. Ricco Rakotomalala – Université Lyon 2

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.

How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did Target has got you in its aim Every time you go shopping, you share intimate details about your consumption patterns with retailers. And many of those retailers are studying those details to figure out what you like, what you need, and which coupons are most likely to make you happy. Target, for example, has figured out how to data-mine its way into your womb, to figure out whether you have a baby on the way long before you need to start buying diapers. Charles Duhigg outlines in the New York Times how Target tries to hook parents-to-be at that crucial moment before they turn into rampant — and loyal — buyers of all things pastel, plastic, and miniature. He talked to Target statistician Andrew Pole — before Target freaked out and cut off all communications — about the clues to a customer’s impending bundle of joy. [Pole] ran test after test, analyzing the data, and before long some useful patterns emerged. Or have a rather nasty infection… Target knows before it shows. Bold is 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.

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: Text Mining, Visualization and Social Media 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]

Microsoft Makes Data Mining Personal - Tech Europe By Nick Clayton The idea of “data mining” has become quite closely associated with the idea of companies such as Facebook or Google sifting through vast quantities of data, generally to find some sort of purchasing sentiment. Microsoft has created a project which uses similar methods but in a way that is rather more personal, smaller scale and local. The Technology Review published by MIT explains: Software called Lifebrowser processes photos, e-mails, Web browsing history, calendar events, and other documents stored on a person’s computer and identifies landmark events. Technology Review: Microsoft Builds a Browser for Your Past