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Encyclopedia results for Data mining

Data mining





Encyclopedia results for Data mining

  1. Data mining

    Distinguish analytics information extraction data analysis Data mining the analysis step of the Knowledge ... Definition of Data Mining year 2010 url http www.britannica.com EBchecked topic 1056150 data ... , statistics , and database system s. ref name acm The overall goal of the data mining process is to extract ... Data mining Practical machine learning tools and techniques with Java ref name witten cite book author1 Ian H. Witten author2 Eibe Frank author3 Mark A. Hall title Data Mining Practical Machine Learning ... , and the term data mining was only added for marketing reasons. ref Cite journal title WEKA ... machine learning , was changed ... The term data mining was added primarily for marketing reasons. postscript ... appropriate. The actual data mining task is the automatic or semi automatic analysis of large ... learning and predictive analytics . For example, the data mining step might identify multiple ... are part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging , data fishing , and data snooping refer to the use of data mining ... tree learning decision trees 1960s , and support vector machines 1990s . Data mining is the process ... Kantardzic first Mehmed title Data Mining Concepts, Models, Methods, and Algorithms year 2003 publisher ... Discovery and Data Mining SIGKDD . Since 1989 they have hosted an annual international conference and published ... on Knowledge Discovery and Data Mining, ACM, New York. ref and since 1999 have published a biannual ... Explorations , ACM, New York. ref Computer science conferences on data mining include CIKM ACM Conference on Information and Knowledge Management DMIN International Conference on Data Mining DMKD Research Issues on Data Mining and Knowledge Discovery ECDM European Conference on Data Mining ECML ... EDM International Conference on Educational Data Mining ICDM IEEE International Conference on Data Mining KDD ACM SIGKDD Conference on Knowledge Discovery and Data Mining MLDM Machine Learning ...   more details



  1. Relational data mining

    Relational data mining is the data mining technique for relational databases. Unlike traditional data mining algorithms, which look for patterns in a single table propositional patterns , relational data mining algorithms look for patterns among multiple tables relational patterns . For most types of propositional propatterns, there are corresponding relational patterns. For example, there are relational classification rule s, relational Decision tree learning regression tree , relational association rule s, and so on. The most important theoretical foundation of relational data mining is inductive logic programming . See also Data mining Database mining Structured data mining External links http www ai.ijs.si SasoDzeroski RDMBook Web page for a text book on relational data mining Category Data collection Category Relational model database stub ...   more details



  1. Java Data Mining

    Java Data Mining JDM is a standard Java API for developing data mining applications and tools. JDM defines an object model and Java API for data mining objects and processes. JDM enables applications to integrate data mining technology for developing predictive analytics applications and tools. The JDM 1.0 standard was developed under the Java Community Process as JSR 73. In 2006, the JDM 2.0 specification was being developed under JSR 247, but has been withdrawn in 2011 without standardization. Various data mining functions and techniques like statistical classification and association statistics association , regression analysis , data clustering , and attribute importance are covered by the 1.0 release of this standard. See also AIDA computing AIDA Abstract Interfaces for Data Analysis is a language neutral standard, with a Java implementation Mark F. Hornick, Erik Marcade, Sunil Venkayala Java Data Mining Strategy, Standard, And Practice A Practical Guide for Architecture, Design, And Implementation Broch jHepWork Java data analysis and data mining framework Weka machine learning R programming language SPSS Apache Mahout Books cite Java Data Mining Strategy, Standard, and Practice cite , Morgan Kaufmann, ISBN 0 12 370452 9 External links http www.jcp.org en jsr detail?id 247 JSR 247 JDM 2.0 http www.jcp.org en jsr detail?id 73 JSR 73 JDM 1.0 https datamining.dev.java.net Datamining ... Java Data Mining concepts article by Mark F. Hornick, Erik Marcad , and Sunil Venkayala, at JavaWorld.com http www.artima.com lejava articles data mining.html Mine Your Own Data with the JDM API article by Frank Sommers http jdj.sys con.com read 49091.htm Using Java Data Mining to Develop Advanced Analytics Applications article by Sunil Venkayala at SYS CON JDM Article Category Java platform Data Mining Category Java specification requests Data Mining Category Applied data mining Compu lang stub es Java Data Mining fr Java Data Mining ...   more details



  1. Data Mining Extensions

    Data Mining Extensions DMX is a query language for Data Mining Models supported by Microsoft s SQL Server Analysis Services product. Like SQL, it supports a data definition language, data manipulation language and a data query language, all three with SQL like syntax. Whereas SQL statements operate on relational tables, DMX statements operate on data mining models. Similarly, SQL Server supports the Multidimensional Expressions MDX language for OLAP databases. DMX is used to create and train data mining models, and to browse, manage, and predict against them. DMX is composed of data definition language DDL statements, data manipulation language DML statements, and functions and operators. DMX Queries DMX Queries are formulated using the code SELECT code statement. They can extract information from existing data mining models in various ways. Data Definition Language The Data Definition Language DDL part of DMX can be used to Create new data mining models and mining structures code CREATE MINING STRUCTURE, CREATE MINING MODEL code Delete existing data mining models and mining structures code DROP MINING STRUCTURE, DROP MINING MODEL code Export and import mining structures code EXPORT, IMPORT code Copy data from one mining model to another code SELECT INTO code Data Manipulation Language The Data Manipulation Language DML part of DMX can be used to Train mining models code INSERT INTO code Browse data in mining models code SELECT FROM code Make predictions using mining model code SELECT ... FROM PREDICTION JOIN code Example a prediction query This example is a singleton prediction ... links http msdn2.microsoft.com en us library ms132058.aspx Data Mining Extensions DMX Reference , at MSDN http www.sqlserverdatamining.com SQL Server Data Mining http blogs.msdn.com jamiemac Jamie MacLennan s Blog Data warehouse Query languages Category Query languages Category Data mining and machine learning software de Data Mining Extensions pl Data Mining Extensions ...   more details



  1. Oracle Data Mining

    Oracle Data Mining ODM is an option of Oracle Corporation s Relational Database Management System RDBMS Enterprise Edition EE . It contains several data mining and data analysis algorithms for Statistical ... analytics. It provides means for the creation, management and operational deployment of data mining models inside the database environment. Infobox Software name Oracle Data Mining logo screenshot Deleted image removed Image ODMr 360 customer view workflow ss.jpg 700px Oracle Data Mining GUI caption Oracle Data Mining developer Oracle Corporation latest release version 11gR2 latest release date September, 2009 genre data mining and analytics license Proprietary software proprietary website ref http www.oracle.com technology products bi odm index.html ref Overview Oracle Data Mining implements a variety of data mining algorithms inside the Oracle database Oracle relational database . These implementations ... transfer of data into standalone mining analytic server computing servers . The relational database ... unified interface for data mining functions. These operations include functions to Data Definition Language create , apply , Test method test , and Data manipulation manipulate data mining models ... similar to tables, views, indexes and other database objects. In data mining, the process of using ... problem is also provided. Most Oracle Data Mining functions also allow text mining by accepting Text unstructured data attributes as input. History Oracle Data Mining was first introduced in 2002 and its releases are named according to the corresponding Oracle database release Oracle Data Mining 9iR2 9.2.0.1.0 May 2002 Oracle Data Mining 10gR1 10.1.0.2.0 February 2004 Oracle Data Mining 10gR2 10.2.0.1.0 July 2005 Oracle Data Mining 11gR1 11.1 September 2007 Oracle Data Mining 11gR2 11.2 September 2009 Oracle Data Mining is a logical successor of the Darwin data mining toolset developed ... a data mining development deployment platform integrated into the Oracle database, along with the GUI ...   more details



  1. Educational data mining

    Data Mining called EDM is an emerging discipline, concerned with developing methods for exploring ... of Educational Data Mining in 2009 A Review and Future Visions journal Journal of Educational Data Mining, Volume 1, Issue 1 year 2010 pages 3 17 volume 1 ref Another key area is mining enrollment data. ref Cite journal author C. Romero, S. Ventura, E. Garcia title Data Mining in Course Management ... one of the learning sciences , as well as an area of data mining . A related field is learning analytics . EDM methods The types of EDM method are related to those found in data mining in general ... 2010 Data Mining for Education. In McGaw, B., Peterson, P., Baker, E. Eds. International Encyclopedia ... clustering Correlation clustering data mining Correlation mining Sequential pattern mining Causal data mining Distillation of data for human judgment Discovery with models Baker and Kalina Yacef claim that discovery with models is particularly prominent in EDM, as compared to data mining in general ... . ref C. Romero, S. Ventura. Educational Data Mining A Review of the State of the Art. IEEE Transaction ... of EDM work are published at the peer reviewed International Conference on Educational Data Mining, organized by the http www.educationaldatamining.org International Educational Data Mining Society . http www.educationaldatamining.org EDM2008 1st International Conference on Educational Data Mining ... Data Mining 2009 Cordoba, Spain http www.educationaldatamining.org EDM2010 3rd International Conference on Educational Data Mining 2010 Pittsburgh, USA http www.educationaldatamining.org EDM2011 4th International Conference on Educational Data Mining 2011 Eindhoven, Netherlands EDM papers are also published in the http www.educationaldatamining.org JEDM Journal of Educational Data Mining ... of Educational Data Mining in the KDD Cup In 2010, the Association of Computing Machinery s http ... data mining. The data set was provided by the Pittsburgh Science of Learning Center DataShop, and consisted ...   more details



  1. Data Mining and Knowledge Discovery

    Infobox journal title Data Mining and Knowledge Discovery cover discipline Computer science abbreviation Data Min. Knowl. Discov. publisher Springer Science Business Media country frequency Triannually history 1997 present impact 1.238 impact year 2010 website http www.springer.com computer database management & information retrieval journal 10618 link1 http www.springerlink.com content r8044h783172 link1 name Online access ISSN 1384 5810 eISSN 1573 756X CODEN DMKDFD LCCN sn98038132 OCLC 38037443 Data Mining and Knowledge Discovery is a triannual Peer review peer reviewed scientific journal focusing on data mining . It is published by Springer Science Business Media . External links Official 1 http www.springer.com computer database management & information retrieval journal 10618 DEFAULTSORT Data Mining And Knowledge Discovery Category Computer science journals Category Data mining Category Springer academic journals Category Publications established in 1997 Category English language journals Category Triannual journals academic journal stub ...   more details



  1. Evolutionary data mining

    Orphan date October 2011 Evolutionary data mining , or genetic data mining is an umbrella term for any data mining using evolutionary algorithm s. While it can be used for mining data from DNA sequence ...?arnumber 1255389&isnumber 28075 A Novel Evolutionary Data Mining Algorithm With Applications to Churn ... A. http neuro.bstu.by our Data mining fereitas ga.pdf A Survey of Evolutionary Algorithms for Data Mining ... algorithms for data mining work by creating a series of random rules to be checked against a training dataset . ref name jiawei The rules which most closely fit the data are selected and are mutated ... Data mining Evolutionary algorithms work by trying to emulate natural evolution . ref name jiawei First, a random series of rules are set on the training dataset, which try to generalize the data into formulas. ref name jiawei The rules are checked, and the ones that fit the data best are kept ... Data mining Evolutionary algorithm Knowledge discovery Pattern mining Data analysis References reflist Category Data mining Category Data analysis database stub ... that approaches 100 similarity with the training data. ref name freitas This rule is then checked against ... Data preparation Before database s can be mined for data using evolutionary algorithms, it first has to be cleaned, ref name freitas which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results. ref name jiawei Jiawei Han, Micheline Kamber Data Mining Concepts and Techniques 2006 , Morgan Kaufmann , ISBN 1558609016 ref If data comes from more than one database ..., it might be beneficial to also reduce the amount of data being handled. ref name jiawei One common method of data reduction works by getting a Normalization statistics normalized sample of data ... At this point, the data is split into two equal but mutually exclusive elements, a test and a training ...   more details



  1. National Center for Data Mining

    The National Center for Data Mining NCDM is a center of the University of Illinois at Chicago UIC , established in 1998 to serve as a resource for research, standards development, and outreach for high performance and distributed data mining and predictive modeling . NCDM won the High Performance Bandwidth Challenge at SuperComputing 06 in Tampa, FL and recently demonstrated the use of UDP Data Transport . External links http www.ncdm.uic.edu National Center for Data Mining https scinet.supercomp.org 2006 bwc SC06 Bandwidth Challenge Results br University of Illinois at Chicago campus compsci stub Category University of Illinois at Chicago ...   more details



  1. Data mining in agriculture

    Data mining in agriculture is a very recent research topic. It consists in the application of data mining ... on conveyor belt s, and which is also able to analyse by data mining techniques the taken pictures ... usage by data mining Recent studies by agriculture researchers in Pakistan one of the top four ... abuse. ref name ahsan Explaining pesticide abuse by data mining To monitor cotton growth, different ... Data Warehouse followed by analysis through querying and data mining some interesting discoveries ... en book.php Data Mining in Agriculture is published by Springer and it is co authored by http www.antoniomucherino.it ... devoted to data mining in agriculture. Conferences There are many conferences organized every year on data mining techniques and applications, but rather few of them consider problems ... to applications in agriculture of data mining. It is organized by Georg Ru . This is the conference ... last1 Mucherino first1 A. last2 Papajorgji first2 P.J. last3 Pardalos first3 P. title Data Mining ... Conference on Data Mining ICDM10 , Workshop Data Mining in Agriculture DMA10 , Springer pages ... jrpit cite journal last1 Abdullah first1 Ahsan last2 Hussain first2 Amir title Data Mining a New ... Azhar title Learning Dynamics of Pesticide Abuse through Data Mining conference Australasian Workshop on Data Mining and Web Intelligence, Dunedin, New Zealand year 2004 pages url http crpit.com confpapers CRPITV32Abdullah.pdf ref DEFAULTSORT Data Mining In Agriculture Category Applied data mining ... of pesticides is harming the farmers with adverse financial, environmental and social impacts. By data mining the cotton Pest Scouting data along with the meteorological recordings it was shown that how pesticide usage can be optimized reduced . Clustering of data revealed interesting patterns of farmer ... and metrological data for decades. Coarse estimates of just the cotton pest scouting data recorded stands at around 1.5 million records, and growing. The primary agro met data recorded has never been ...   more details



  1. Data stream mining

    Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining ... change over time. This problem is referred to as concept drift . Software for data stream mining RapidMiner free open source software for knowledge discovery, data mining, and machine learning also featuring data stream mining, learning time varying concepts, and tracking drifting concept if used in combination with its data stream mining plugin formerly concept drift plugin MOA http sourceforge.net projects moa datastream Massive Online Analysis free open source software specific for mining data ... Workshop on Mining Evolving and Streaming Data IWMESD 2006 to be held in conjunction with the http www.comp.hkbu.edu.hk wii06 icdm 2006 IEEE International Conference on Data Mining ICDM 2006 in Hong Kong ... 2006. Researchers working on data stream mining http cs.ucla.edu zaniolo Carlo Zaniolo , University ..., S., Mining Data Streams A Review , in ACM SIGMOD Record, Vol. 34, No. 1, June 2005, ISSN 0163 5808 ... , in Proceedings of PODS, 2002. http www.csse.monash.edu.au mgaber WResources.htm Mining Data Streams ... C., and Cardona C., A Framework for Mining Evolving Trends in Web Data Streams using Dynamic Learning ... Press, 2010. See also Data Mining Sequence mining Streaming Algorithm Stream processing Wireless sensor ... programming language and compilation infrastructure by MIT CSAIL DEFAULTSORT Data Stream Mining Category Data mining Category Business intelligence fr Fouille de flots de donn es .... Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining , machine learning , and knowledge discovery . In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership ...   more details



  1. Wrapper (data mining)

    Wrapper in data mining is a program that extracts content of a particular information source and translates it into a Relational model relational form . ref Nicholas Kushmerick, Daniel S. Weld, Robert Doorenbos, http www.isi.edu info agents courses iiweb kushmerick ijcai97.pdf Wrapper Induction for Information Extraction Proceedings of the International Joint Conference on Artificial Intelligence, 1997 ref Many web pages present structured data telephone directories, product catalogs, etc. formatted for human browsing using HTML language. Structured data are typically descriptions of objects retrieved from underlying databases and displayed in Web pages following some fixed templates. Software systems using such resources must translate HTML content into a relational form. Wrappers are commonly used as such translators. Formally, a wrapper is a function from a page to the set of Tuple tuples it contains. Wrapper generation There are two main approaches to wrapper generation wrapper induction and automated data extraction. Wrapper induction uses supervised learning to learn data extraction rules from manually labeled training examples. The disadvantages of wrapper induction are the time consuming manual labeling process and the difficulty of wrapper maintenance. Due to the manual labeling effort, it is hard to extract data from a large number of sites as each site has its own templates ... Data Mining Exploring Hyperlinks, Contents and Usage Data , Springer, 2007. ref Wrapper generation on the Web is an important problem with a wide range of applications. Extraction of such data enables one to integrate data information from multiple Web sites to provide value added services, e.g. ..., researchers have studied automated wrapper generation using unsupervised pattern mining. Automated extraction is possible because most Web data objects follow fixed templates. Discovering ... Semi structured or unstructured data Web scraping Sources references Category Internet de Wrapper ...   more details



  1. Data mining in meteorology

    About other uses of the root word meteor Meteor disambiguation about data mining Data mining Meteorology ... this issue, it is necessary to first analyze and simplify the data before proceeding with other analysis. Some data mining techniques are appropriate in this context. What is Data mining? Data mining , the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to analyze important information in data warehouses. Consequently, data mining consists of more than collecting and analyzing data, it also includes analyze and predictions .... title Data Mining An Overview journal CRS year 2004 ref The network architecture and signal process ..., M. title IMPLEMENTATION OF DATA MINING TECHNIQUES FOR METEOROLOGICAL APPLICATIONS journal World ... External links Categories Category Meteorology Category Applied data mining Category Articles created ..., respectively. There are many methods of collecting data and Radar , Lidar , satellites are some of them. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere ... network models, which is especially suitable for high dimensional data visualization, clustering ... the data. The SOM was introduced to meteorological and climatic sciences in late 1990s as a clustering ... 1852. volume 43 ref classification of TEMP data, ref cite news author Lahoz, D., & Miguel, M. S. title CLASSIFICATION TEMP DATA WITH SELF ORGANIZING MAPS. journal Monograf as del Seminario Matem tico ... Organizing Map projects high dimensional input data onto a low dimensional usually two dimensional ... Design . pages 2011. ref Because it preserves the neighborhood relations of the input data, the SOM ... DATA WITH SELF ORGANIZING MAPS. journal Monograf as del Seminario Matem tico Garc a de Galdeano date ..., M. S. title CLASSIFICATION TEMP DATA WITH SELF ORGANIZING MAPS. journal Monograf as del Seminario ...., & Miguel, M. S. title CLASSIFICATION TEMP DATA WITH SELF ORGANIZING MAPS. journal Monograf as del ...   more details



  1. Lift (data mining)

    Other uses Lift disambiguation Lift In data mining and association rule learning , lift is a measure of the performance of a model abstract targeting model association rule at predicting or classifying cases as having an enhanced response with respect to the population as a whole , measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values target response divided by average response. For example, suppose a population has an average response rate of 5 , but a certain model or rule has identified a segment with a response rate of 20 . Then that segment would have a lift of 4.0 20 5 . Typically, the modeller seeks to divide the population into quantile s, and rank the quantiles by lift. Organizations can then consider each quantile, and by weighing the predicted response rate and associated financial benefit against the cost ... in econometrics as the Lorenz curve Lorenz or power curve. ref Tuff ry, St phane 2011 Data Mining ... Data Mining et statistique d cisionnelle ditions Technip, 2008 ref Example Assume the data set being ... Data mining fr Courbe lift .... Real mining problems would typically have more complex antecedents, but usually focus on single value consequents. Most mining algorithms would determine the following rules targeting models Rule 1 A implies 0 Rule 2 B implies 1 because these are simply the most common patterns found in the data ... those rules potentially useful for predicting the consequent in future data sets. Observe that even ... of the rule independent of the data set can be misleading. The value of lift is that it considers both the confidence of the rule and the overall data set. References references cite news last Coppock first David S. url http www.dmreview.com article sub.cfm?articleId 5329 title Data Modeling ...   more details



  1. Cross Industry Standard Process for Data Mining

    CRISP DM stands for Cross Industry Standard Process for Data Mining ref name Shearer00 Shearer C. http www.crisp dm.org News 86605.pdf The CRISP DM model the new blueprint for data mining . J Data Warehousing 2000 5 13 22. ref . It is a data mining process model that describes commonly used approaches that expert data miners use to tackle problems. Polls conducted in 2002, 2004, and 2007 show that it is the leading methodology used by data miners. ref Gregory Piatetsky Shapiro 2002 http www.kdnuggets.com polls 2002 methodology.htm KDnuggets Methodology Poll ref ref Gregory Piatetsky Shapiro 2004 http www.kdnuggets.com polls 2004 data mining methodology.htm KDnuggets Methodology Poll ref ref Gregory Piatetsky Shapiro 2007 http www.kdnuggets.com polls 2007 data mining methodology.htm KDnuggets Methodology Poll ref Major phases CRISP DM breaks the process of data mining into six major phases ... August title Methods for mining HTS data journal Drug Discovery Today volume 11 issue 15 16 pages 694 ... Inc. The computer giant NCR Corporation produced the Teradata data warehouse and its own data mining software. Daimler Benz had a significant data mining team. http www.ohra.nl OHRA , an insurance company, was just starting to explore the potential use of data mining. The first version of the methodology ... Anchors the data mining process References div class references small references div External links http crispdm.wordpress.com CRoss Industry Standard Process for Data Mining Blog http lesitedesdataminers.free.fr ... Pascal BIZZARI, Mai 2009 http www.dmg.org The Data Mining Group DMG The DMG is an independent, vendor led group which develops data mining standards, such as the Predictive Model Markup Language PMML Category Applied data mining Comp sci stub de CRISP DM pt Cross Industry Standard Process for Data Mining fr Cross Industry Standard Process for Data Mining ... pmid 16846796 ref Business Understanding Data Understanding Data Preparation Modeling Evaluation ...   more details



  1. Mining

    Other uses Image Simplified world mining map 2.png thumb 500px right Simplified world active mining map ... circumference and second deepest open pit mining open pit copper mine in the world. Mining is the extraction ..., from an ore body, vein geology vein or coal mining coal seam. The term also includes the removal of soil. Materials recovered by mining include base metals , precious metals , iron , uranium , coal , diamond s, limestone , oil shale , Sodium chloride rock salt and potash . Mining is required ... Chemical synthesis artificially in a laboratory or factory . Mining in a wider sense comprises extraction of any non renewable resource e.g., petroleum , natural gas , or even fossil water water . Mining of stone and metal has been done since Prehistory pre historic times. Modern mining processes involve ... is closed. The nature of mining processes creates a potential negative impact on the environment both during the mining operations and for years after the mine is closed. This impact has led to most of the world s nations adopting regulations to moderate the negative effects of mining operations .... History Prehistoric mining Image TimnaChalcolithicMine.JPG thumb Chalcolithic copper mine ... Mining Engineering Handbook , Society for Mining, Metallurgy, and Exploration Inc, 1992, p3. ref Flint ... Shaw on the Sinai Peninsula and at Timna Valley Timna . Mining in Egypt occurred in the earliest dynasties ... dust. Ancient Greece and Rome See also Mining in Roman Britain Image Dolaucothimap4.jpg thumb 250px right Ancient Roman development of the Dolaucothi Gold Mines , Wales. Mining in Europe has a very ... City state city state of Athens . However, it is the Ancient Rome Romans who developed large scale mining ... debris, called hydraulic mining , as well as washing comminution comminuted or crushed ores, and driving simple machinery. The Romans used hydraulic mining methods on a large scale to prospect for the vein s of ore, especially a now obsolete form of mining known as hushing . It involved building ...   more details



  1. Data

    mining Data modeling Computer data processing Data remanence Data set Data warehouse Database Datasheet ...two other uses data in computer science Data computing pp move indef Editors please keep the count mass discussion and etymology in the body, not the intro. Data IPAc en icon d e t respell DAY t , IPAc en d t respell DA t , or IPAc en d t respell DAH t are values of Qualitative data qualitative or Quantitative data quantitative variable and attribute research variable s, belonging to a set of items. Data in computing or data processing are often represented by a combination of items organized in rows and multivariate analysis multiple variables organized in columns. Data are typically the results of measurements and can be data visualisation visualised using graph data structure graph s or image s. Data as an abstract concept can be viewed as the lowest level of abstraction from which information and then knowledge are derived. Raw data , i.e., unprocessed data, refers to a collection of number s, character computing characters and is a relative term data processing commonly occurs by stages, and the processed data from one stage may be considered the raw data of the next. Field work Field data refers to raw data collected in an uncontrolled in situ environment. Experimental data refers to data generated within the context of a scientific investigation by observation and recording. The word data is the plural of datum , Grammatical gender neuter past participle ... , engineering , and so on, the terms givens and data are used interchangeably. Such usage is the origin of data as a concept in computer science or data processing data are numbers, words, images ... datum from which distances to all other data are measured. Any measurement or result is a datum , but data point is more usual, ref cite web author Matt Dye year 2001 title Writing Reports url http ... and the originally Latin plural data are used as the plural of datum in English, but data is commonly ...   more details



  1. Mining (disambiguation)

    Mining may refer to Mining , the extraction of geological materials from the Earth. Bitcoin mining , the allocation of processing power for Bitcoin transactions with the expectation of a reward. Data mining , the process of extracting knowledge from a data set. Mining military , a siege tactic. Mining, Austria , a municipality in Upper Austria . Mianning, the Daoguang Emperor , eighth emperor of the Manchurian Qing dynasty. disambig ...   more details



  1. DATA

    otheruses Data disambiguation Debt, AIDS, Trade, Africa or DATA was a multinational Non governmental organization non government organization founded in January 2002 in London by U2 s Bono along with Robert Sargent Shriver III Bobby Shriver and activist s from the Jubilee 2000 Drop the Debt campaign. DATA was created for the purposes of obtaining social equality equality and justice for Africa through debt relief adjusting trade rules which burden Africa eliminating the AIDS in Africa African AIDS epidemic strengthening democracy furthering accountability by the wealthiest nations and African leaders and Transparency humanities transparency towards the people. In 2007, DATA and Bono were jointly awarded the National Constitution Center s 2007 Liberty Medal for their groundbreaking efforts to address the AIDS crisis and extreme poverty in Africa. Start up funds came from the Bill & Melinda Gates Foundation , financier George Soros , and technology entrepreneur Edward W. Scott . ref cite news url http www.time.com time printout 0,8816,1142270,00.html title The Constant Charmer author Josh Tyrangiel date 2005 12 19 work Time Magazine ref In 2007, DATA and the ONE Campaign decided to join forces, and in January 2008, they formally merged under the name ONE. ref cite news url http www.reuters.com article idUSN2953856520071029 title Bono s U.S. based anti poverty groups to merge author Lesley Wroughton date 2007 10 29 work Reuters ref DATA received support from the Christian rock Alternative rock bands Switchfoot and Third Day . References references External links http www.one.org ONE official site Category International nongovernmental organizations Category HIV AIDS in Africa Category Organizations founded by Bono int org stub de Debt, AIDS, Trade in Africa it DATA sv DATA f rening vi DATA ...   more details



  1. Structure mining

    Structure mining or structured data mining is the process of finding and extracting useful information from semi structured data sets. Graph mining is a special case of structured data mining Citation needed date November 2010 . Description The growth of the use of semi structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases . Much of the world s interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML , being the most frequent way of representing semi structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally ... on what is being transmitted. Such data presents large problems for conventional data mining. Two ... from such data means that if one were to try to format it as tabular data for conventional data mining ... of most data mining algorithms that the data presented will be complete. Many algorithms perform ... two extensions are required to conventional data mining. These are the ability to associate an XPath ... the set of data mining algorithms that are best at handling sparse data are those that process the training ... mining Sequence mining Data mining Data warehousing Structured content External links http mlg07.dsi.unifi.it ... References http www.scientio.com documents XmlMiner structureminingpaper Andrew N Edmonds, On data mining tree structured data in XML , Data mining UK conference, University of Nottingham, Aug 2003 Gusfield ... , John Wiley & Sons, 2001 . ISBN 0 471 05669 3 DEFAULTSORT Structure Mining Category Data ... for representing special case data. Frequently around 90 of a Schema is concerned with the definition ...   more details



  1. Outline of mining

    www.mining journal.com Mining Journal http media.wiley.com product data excerpt 11 04713485 0471348511.pdf ...The following outline is provided as an overview of and topical guide to mining Mining &ndash extraction ... or factory , is usually mined. Essence of mining Main article Mining Extractive metallurgy Mineral exploration Mining engineering Ore Ore dressing Ore genesis Overburden Prospecting Tailings Resource extraction Vein geology Types of mining Surface mining Sub surface mining Mining techniques Alpha Hydraulic Diggings Borehole mining Box cut Deepsea mining Dredging Drift mining Fire setting Glory hole petroleum production Heap leaching Hydraulic mining In situ leach Landfill mining Longwall mining Mine reclamation Mountaintop removal mining Omega Hydraulic Diggings Open pit mining Placer mining Quarry ing Quartz reef mining Retreat mining Room and pillar Shaft mining Slope mining Stoping mining method Stoping Strip mining Sulfide mining Underground mining hard rock Winding tower Materials mined Some examples of materials that are extracted from the earth by mining include Bauxite Chromite ... and purification Nickel extraction and purification Phosphate Precious metal s Gold see Gold mining Silver see Silver mining Oil shale see Oil shale industry and Shale oil extraction Sand see Sand mining Slate see Slate industry Sodium chloride Rock salt Tin Uranium Mining equipment Archimedes screw ... engine Mining hazards and safety Acid mine drainage Bootleg mining Claustrophobia Coal mining debate Damp mining After damp Black damp Fire damp Stink damp White damp Energy law Mine disaster Mine exploration Mine fire Mine rescue Mining accident Subsidence Mining induced Mining induced subsidence History of mining Main article Mining History History of mining Cornish stamps Davy lamp De re metallica Fire setting Freeminer Geordie lamp Gold rush History of coal mining Hurrying Hushing Industrial Revolution Mining Mining innovations during the Industrial Revolution School of mines Mining ...   more details



  1. Software mining

    Warehouse Metamodel focuses entirely on mining enterprise metadata . Text Mining Software Tools Text Mining Software Tools enable easy handling of text documents for the purpose of data analysis including ... the results of Software Mining data model metadata Metamodeling metamodels ontology Knowledge ... program analysis tools Category Data mining ...Software mining is an application of knowledge discovery in the area of software modernization which involves understanding existing software artifacts. This process is related to a concept of reverse engineering . Usually the knowledge obtained from existing software is presented in the form of models to which specific queries can be made when necessary. An entity relationship is a frequent format of representing knowledge obtained from existing software. Object Management Group OMG developed specification Knowledge Discovery Metamodel KDM which defines an ontology for software assets and their relationships for the purpose of performing knowledge discovery of existing code. Software mining and data mining Software mining is closely related to data mining , since existing software artifacts contain enormous business value, key for the evolution of software systems. Knowledge discovery from software systems addresses structure, behavior as well as the data processed by the software system. Instead of mining individual data set s, software mining focuses on metadata , such as database ... Leskovec, Blaz Novak. Levels of software mining Knowledge discovery in software is related to a concept of reverse engineering . Software mining addresses structure, behavior as well as the data processed by the software system. Mining software systems may happen at various levels program level individual ... architectural level subsystems and their interfaces data level individual columns and attributes of data stores application level key data items and their flow through the applications ...   more details



  1. Web mining

    Cleanup date June 2009 Rewrie whole article? Web mining is the application of data mining techniques ... in multimedia data. Web structure mining Web structure mining is the process of using graph ... structural data, web structure mining can be divided into two kinds 1. Extracting patterns from hyperlinks ... HTML or XML tag usage. Web content mining Mining, extraction and integration of useful data ... and data mining techniques to provide a higher level of organization for semi structured data ... data mining are very similar to traditional data mining techniques. The usual evaluative merits ... of the HTML documents and transform it into inner code, then use other data mining techniques ... journal year 2004 author Lita van Wel and Lamb r Royakkers title Ethical issues in web data mining url http alexandria.tue.nl repository freearticles 612259.pdf journal Ethical issues in web data mining ... them from trading the data. Some mining algorithms might use controversial attributes like sex, race ... in Data Mining url http www.cis.unisa.edu.au ciskw WahlstromRoddickSarreEstivillCastro&DeVries2007.pdf journal Legal and Technical Issues of Privacy Preservation in Data Mining . ref The applications ... can be avoided by the high ethical standards maintained by the data mining company. The collected ... Directory Compare and review web mining programs Books Jesus Mena, Data Mining Your Website , Digital Press, 1999 Soumen Chakrabarti, Mining the Web Analysis of Hypertext and Semi Structured Data , Morgan Kaufmann, 2002 Bing Liu, http www.cs.uic.edu liub WebMiningBook.html Web Data Mining Exploring Hyperlinks, Contents and Usage Data , Springer, 2007 Advances in Web Mining and Web Usage Analysis ... Intelligence Cooley, R., Mobasher, B. and Srivastava, J. Data Preparation for Mining World ... World Wide Web Personalization, Invited chapter in Encyclopedia of Data Mining and Data ... Mining Finding Unexpected Browsing Behaviour in Clickstream Data to improve a Web Site s Design References ...   more details



  1. Sequence mining

    Sequence mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. ref cite doi 10.1145 1824795.1824798 ref It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity. Sequence mining is a special case of structured data mining . There are several key traditional computational problems addressed within this field. These include ... Mining in Bioinformatics&hl en&as sdt 1 2C5&as sdtp on String Mining in Bioinformatics , ref http scholar.google.co.uk scholar?q String Mining in Bioinformatics&hl en&as sdt 1 2C5&as sdtp on String M. Abouelhoda, M. Ghanem. String Mining in Bioinformatics. In M. M. Gaber Editor Scientific Data Mining ... mining current status and future directions. In Data Mining and Knowledge Discovery Volume 15 ... databases for Association rule learning frequent itemset mining are the influential apriori algorithm and the more recent FP Growth technique. See also Association rule learning Data Mining GSP ... Category Data mining Category Bioinformatics Category Bioinformatics algorithms de Sequenzmuster .... In general, sequence mining problems can be classified as string mining which is typically based on string computer science string processing algorithms and itemset mining which is typically based on association rule learning . String Mining String mining typically deals with a limited alphabet for items ... alignment. See sequence alignment . Itemset Mining Some problems in sequence mining lend themselves ... 2 days . Traditionally, itemset mining is used in marketing applications for discovering regularities ... . A survey and taxonomy of the key algorithms for item set mining is presented in the paper http scholar.google.co.uk scholar?hl en&q Frequent pattern mining 3A current status and future directions &as sdt 0 2C5&as ylo &as vis 0 Frequent pattern mining current status and future directions . ref ...   more details



  1. Report mining

    Report mining is the extraction of data from human readable computer reports. Conventional data extraction requires a connection to a working source system, suitable Database connection connectivity standards or an Application programming interface API , and usually complex querying. By using the source system s standard reporting options, and directing the output to a Spooling spool file instead of to a printer computing printer , static reports can be generated suitable for offline analysis via report mining ref Scott Steinacher, DataPump transforms host data , InfoWorld, 30 August 1999, p55 ref . This approach can avoid intensive Central processing unit CPU usage during business hours, can minimise end user licence costs for Enterprise resource planning ERP customers, and can offer very rapid prototyping and development of custom reports. See also Extraction and Reporting Language These are languages that are commonly used for report mining. References See Wikipedia Footnotes on how to create references using ref ref tags which will then appear here automatically Reflist External links http www.datawatch.com datawatch.com http www.astera.com solutions technology solutions report mining Astera Report Mining http www.monarchforums.com Monarch User Forums http www.excelwithmonarch.com Excel with Monarch Categories Category Data analysis ...   more details




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