@prefix dbpprop: .
@prefix dbpedia: .
dbpedia:Data_cube dbpprop:forProperty dbpedia:Data_mining .
@prefix rdf: .
@prefix opencyc: .
dbpedia:Data_mining rdf:type opencyc:Mx4rHIBS0h_TEdaAAABQ2rksLw .
@prefix owl: .
@prefix ns5: .
dbpedia:Data_mining owl:sameAs ns5:Mx4rvtlZKJwpEbGdrcN5Y29ycA ,
,
opencyc:Mx4rvtlZKJwpEbGdrcN5Y29ycA .
@prefix foaf: .
@prefix ns7: .
dbpedia:Data_mining foaf:page ns7:Data_mining ;
dbpprop:reference .
@prefix rdfs: .
dbpedia:Data_mining rdfs:label "Adatb\u00E1ny\u00E1szat"@hu ,
"Data mining"@cs ,
"Tiedonlouhinta"@fi ,
"Eksploracja danych"@pl ,
"Data mining"@en ,
"Informationsutvinning"@sv ,
"Data mining"@it ,
"Exploration de donn\u00E9es"@fr ,
"Veri madencili\u011Fi"@tr ,
"Miner\u00EDa de datos"@es ,
"Exploatarea datelor"@ro ,
"Datamining"@nl ,
"\u0418\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0443\u0430\u043B\u044C\u043D\u044B\u0439 \u0430\u043D\u0430\u043B\u0438\u0437 \u0434\u0430\u043D\u043D\u044B\u0445"@ru ,
"\u6570\u636E\u6316\u6398"@zh ,
"Minera\u00E7\u00E3o de dados"@pt ,
"Mineria de dades"@ca ,
"\u0414\u043E\u0431\u0443\u0432\u0430\u043D\u043D\u044F \u0434\u0430\u043D\u0438\u0445"@uk ,
"Data Mining"@de ,
"\u30C7\u30FC\u30BF\u30DE\u30A4\u30CB\u30F3\u30B0"@ja ,
"Data mining"@no ;
dbpprop:abstract "Basit bir tan\u0131m yapmak gerekirse veri madencili\u011Fi, b\u00FCy\u00FCk \u00F6l\u00E7ekli veriler aras\u0131ndan bilgiye ula\u015Fma, bilgiyi madenleme i\u015Fidir. Ya da bir anlamda b\u00FCy\u00FCk veri y\u0131\u011F\u0131nlar\u0131 i\u00E7erisinden gelecekle ilgili tahminde bulunabilmemizi sa\u011Flayabilecek ba\u011F\u0131nt\u0131lar\u0131n bilgisayar program\u0131 kullanarak aranmas\u0131d\u0131r. Veri madencili\u011Fi deyimi yanl\u0131\u015F kullan\u0131lan bir deyim olabilece\u011Finden buna e\u015F de\u011Fer ba\u015Fka kullan\u0131mlar da literat\u00FCre ge\u00E7mi\u015Ftir. Veritabanlar\u0131nda bilgi madencili\u011Fi (\u0130ng. knowledge mining from databases), bilgi \u00E7\u0131kar\u0131m\u0131, veri ve \u00F6r\u00FCnt\u00FC analizi, veri arkeolojisi gibi. Bunlar\u0131n aras\u0131ndaki en yayg\u0131n kullan\u0131m Veritabanlar\u0131nda Bilgi Ke\u015Ffi (\u0130ng. VBK - Knowledge Discovery From Databases - KDD)'dir. Alternatif olarak veri madencili\u011Fi asl\u0131nda bilgi ke\u015Ffi s\u00FCrecinin bir par\u00E7as\u0131 \u015Feklinde kabul g\u00F6rmektedir. Bu ad\u0131mlar: Veri temizleme (g\u00FCr\u00FClt\u00FCl\u00FC ve tutars\u0131z verileri \u00E7\u0131karmak) Veri b\u00FCt\u00FCnle\u015Ftirme (bir\u00E7ok veri kayna\u011F\u0131n\u0131 birle\u015Ftirebilmek) Veri se\u00E7me (yap\u0131lacak olan analizle ilgili olan verileri belirlemek) Veri d\u00F6n\u00FC\u015F\u00FCm\u00FC (verinin veri madencili\u011Fi tekni\u011Finden kullan\u0131labilecek hale d\u00F6n\u00FC\u015F\u00FCm\u00FCn\u00FC ger\u00E7ekle\u015Ftirmek) Veri madencili\u011Fi (veri \u00F6r\u00FCnt\u00FClerini yakalayabilmek i\u00E7in ak\u0131ll\u0131 metotlar\u0131 uygulamak) \u00D6r\u00FCnt\u00FC de\u011Ferlendirme (b\u00E2z\u0131 \u00F6l\u00E7\u00FCmlere g\u00F6re elde edilmi\u015F bilgiyi temsil eden ilgin\u00E7 \u00F6r\u00FCnt\u00FCleri tan\u0131mlamak) Bilgi sunumu (m\u00E2dencili\u011Fi yap\u0131lm\u0131\u015F olan elde edilmi\u015F bilginin kullan\u0131c\u0131ya sunumunu ger\u00E7ekle\u015Ftirmek). Veri madencili\u011Fi ad\u0131m\u0131, kullan\u0131c\u0131 ve bilgi taban\u0131yla etkile\u015Fim halindedir. \u0130lgin\u00E7 \u00F6r\u00FCnt\u00FCler kullan\u0131c\u0131ya g\u00F6sterilir, ve bunun \u00F6tesinde istenirse bilgi tabn\u0131na da kaydedilebilir. Buna g\u00F6re, veri madencili\u011Fi i\u015Flemi, gizli kalm\u0131\u015F \u00F6r\u00FCnt\u00FCler bulunana kadar devam eder. Bir veri madencili\u011Fi sistemi, a\u015Fa\u011F\u0131daki temel bile\u015Fenlere sahiptir: Veritaban\u0131, veri ambar\u0131 ve di\u011Fer depolama teknikleri Veritaban\u0131 ya da Veri Ambar\u0131 Sunucusu Bilgi Taban\u0131 Veri Madencili\u011Fi Motoru \u00D6r\u00FCnt\u00FC De\u011Ferlendirme Kullan\u0131c\u0131 Aray\u00FCz\u00FC Veri madencili\u011Fi, eldeki verilerden \u00FCst\u00FC kapal\u0131, \u00E7ok net olmayan, \u00F6nceden bilinmeyen ancak potansiyel olarak kullan\u0131\u015Fl\u0131 bilginin \u00E7\u0131kar\u0131lmas\u0131d\u0131r. Bu da; k\u00FCmeleme, veri \u00F6zetleme, de\u011Fi\u015Fikliklerin analizi, sapmalar\u0131n tespiti gibi belirli say\u0131da teknik yakla\u015F\u0131mlar\u0131 i\u00E7erir. Ba\u015Fka bir deyi\u015Fle, veri madencili\u011Fi, verilerin i\u00E7erisindeki desenlerin, ili\u015Fkilerin, de\u011Fi\u015Fimlerin, d\u00FCzensizliklerin, kurallar\u0131n ve istatistiksel olarak \u00F6nemli olan yap\u0131lar\u0131n yar\u0131 otomatik olarak ke\u015Ffedilmesidir. Temel olarak veri madencili\u011Fi, veri setleri aras\u0131ndaki desenlerin ya da d\u00FCzenin, verinin analizi ve yaz\u0131l\u0131m tekniklerinin kullan\u0131lmas\u0131yla ilgilidir. Veriler aras\u0131ndaki ili\u015Fkiyi, kurallar\u0131 ve \u00F6zellikleri belirlemekten bilgisayar sorumludur. Ama\u00E7, daha \u00F6nceden fark edilmemi\u015F veri desenlerini tespit edebilmektir. Veri madencili\u011Fini istatistiksel bir y\u00F6ntemler serisi olarak g\u00F6rmek m\u00FCmk\u00FCn olabilir. Ancak veri madencili\u011Fi, geleneksel istatistikten birka\u00E7 y\u00F6nde farkl\u0131l\u0131k g\u00F6sterir. Veri madencili\u011Finde ama\u00E7, kolayl\u0131kla mant\u0131ksal kurallara ya da g\u00F6rsel sunumlara \u00E7evrilebilecek nitel modellerin \u00E7\u0131kar\u0131lmas\u0131d\u0131r. Bu ba\u011Flamda, veri madencili\u011Fi insan merkezlidir ve bazen insan \u2013 bilgisayar aray\u00FCz\u00FC birle\u015Ftirilir. Veri madencili\u011Fi sahas\u0131, istatistik, makine bilgisi, veritabanlar\u0131 ve y\u00FCksek performansl\u0131 i\u015Flem gibi temelleri de i\u00E7erir. Veri madencili\u011Fi konusunda bahsi ge\u00E7en geni\u015F verideki geni\u015F kelimesi, tek bir i\u015F istasyonunun belle\u011Fine s\u0131\u011Famayacak kadar b\u00FCy\u00FCk veri k\u00FCmelerini ifade etmektedir. Y\u00FCksek hacimli veri ise, tek bir i\u015F istasyonundaki ya da bir grup i\u015F istasyonundaki disklere s\u0131\u011Famayacak kadar fazla veri anlam\u0131ndad\u0131r. Da\u011F\u0131t\u0131k veri ise, farkl\u0131 co\u011Frafi konumlarda bulunan verileri anlat\u0131r."@tr ,
"Unter Data Mining (englisch f\u00FCr \u201EDatensch\u00FCrfen\u201C) versteht man die systematische Anwendung von Methoden, die meist statistisch-mathematisch begr\u00FCndet sind, auf einen Datenbestand mit dem Ziel der Mustererkennung. Hierbei geht es vor allem um das Durchsuchen sehr gro\u00DFer Datenbest\u00E4nde, weswegen vor allem solche Methoden betrachtet werden, die eine hervorragende asymptotische Laufzeit haben. Bei Verzicht auf Modellannahmen \u00FCber den Datenentstehungsprozess ergeben sich auch bei kleinen oder mittleren Datenbest\u00E4nden sinnvolle Anwendungsm\u00F6glichkeiten. In der Praxis, vor allem im deutschen Sprachgebrauch, etablierte sich der angels\u00E4chsische Begriff \"Data Mining\" f\u00FCr den gesamten Prozess der so genannten \"Knowledge Discovery in Databases\"."@de ,
"La miner\u00EDa de datos (DM, Data Mining) consiste en la extracci\u00F3n no trivial de informaci\u00F3n que reside de manera impl\u00EDcita en los datos. Dicha informaci\u00F3n era previamente desconocida y podr\u00E1 resultar \u00FAtil para alg\u00FAn proceso. En otras palabras, la miner\u00EDa de datos prepara, sondea y explora los datos para sacar la informaci\u00F3n oculta en ellos. Bajo el nombre de miner\u00EDa de datos se engloba todo un conjunto de t\u00E9cnicas encaminadas a la extracci\u00F3n de conocimiento procesable, impl\u00EDcito en las bases de datos. Est\u00E1 fuertemente ligado con la supervisi\u00F3n de procesos industriales ya que resulta muy \u00FAtil para aprovechar los datos almacenados en las bases de datos. Las bases de la miner\u00EDa de datos se encuentran en la inteligencia artificial y en el an\u00E1lisis estad\u00EDstico. Mediante los modelos extra\u00EDdos utilizando t\u00E9cnicas de miner\u00EDa de datos se aborda la soluci\u00F3n a problemas de predicci\u00F3n, clasificaci\u00F3n y segmentaci\u00F3n."@es ,
"Exploatarea datelor (adesea definit\u0103 prin termenul englez Data mining) este un proces de sortare a unor cantit\u0103\u0163i mari de date \u015Fi de extragere a informa\u0163iilor relevante din acestea. Termenul este utilizat de obicei de organiza\u0163iile ce se ocup\u0103 cu prelucrarea informa\u0163iilor companiilor, \u015Fi de anali\u015Ftii financiari, dar este folosit din ce \u00EEn ce mai mult \u015Fi \u00EEn domeniul \u015Ftiin\u0163ific cu referire la extragerea informa\u0163iilor din volumuri mari de date generate de metode experimentale moderne. Data mining a fost descris ca \"extragerea netrivial\u0103 a informa\u0163iilor implicite, anterior necunoscut\u0103 \u015Fi poten\u0163ial util\u0103 din date\" \u015Fi ca \"\u015Ftiin\u0163a extragerii informa\u0163iilor utile din volume de date mari sau din baze de date. \" Data mining, \u00EEn rela\u0163ie cu planificarea resurselor economice, este analiza statistic\u0103 \u015Fi logic\u0103 a unor mari volume de date despre tranzac\u0163ii, \u00EEn c\u0103utarea unor \u015Fabloane care pot ajuta procesul de luare a deciziilor."@ro ,
"L\u2019exploration de donn\u00E9es, aussi connue sous les noms fouille de donn\u00E9es, data mining (forage de donn\u00E9es) ou encore Extraction de Connaissances \u00E0 partir de Donn\u00E9es (ECD en fran\u00E7ais, KDD en Anglais), a pour objet l\u2019extraction d'un savoir ou d'une connaissance \u00E0 partir de grandes quantit\u00E9s de donn\u00E9es, par des m\u00E9thodes automatiques ou semi-automatiques, et l'utilisation industrielle ou op\u00E9rationnelle de ce savoir. Elle est utilis\u00E9e dans le monde professionnel pour r\u00E9soudre des probl\u00E9matiques tr\u00E8s diverses, allant de la gestion de relation client \u00E0 la maintenance pr\u00E9ventive, en passant par la d\u00E9tection de fraudes ou encore l'optimisation de sites web."@fr ,
"Il data mining ha per oggetto l'estrazione di un sapere o di una conoscenza a partire da grandi quantit\u00E0 di dati (attraverso metodi automatici o semi-automatici) e l'utilizzazione industriale o operativa di questo sapere."@it ,
"Data mining (angl. dolov\u00E1n\u00ED z dat \u010Di vyt\u011B\u017Eov\u00E1n\u00ED dat) je analytick\u00E1 metodologie z\u00EDsk\u00E1v\u00E1n\u00ED netrivi\u00E1ln\u00EDch skryt\u00FDch a potenci\u00E1ln\u011B u\u017Eite\u010Dn\u00FDch informac\u00ED z dat. N\u011Bkdy se ch\u00E1pe jako analytick\u00E1 sou\u010D\u00E1st dob\u00FDv\u00E1n\u00ED znalost\u00ED z datab\u00E1z\u00ED (Knowledge Discovery in Databases, KDD), tak nap\u0159\u00EDklad Berka (2003), jindy se tato dv\u011B ozna\u010Den\u00ED ch\u00E1pou jako souzna\u010Dn\u00E1. Data mining se pou\u017E\u00EDv\u00E1 v komer\u010Dn\u00ED sf\u00E9\u0159e (nap\u0159\u00EDklad v marketingu p\u0159i rozhodov\u00E1n\u00ED, kter\u00E9 klienty oslovit dopisem s nab\u00EDdkou produktu), ve v\u011Bdeck\u00E9m v\u00FDzkumu (nap\u0159\u00EDklad p\u0159i anal\u00FDze genetick\u00E9 informace) i v jin\u00FDch oblastech (nap\u0159\u00EDklad p\u0159i monitorov\u00E1n\u00ED aktivit na internetu s c\u00EDlem odhalit \u010Dinnost potenci\u00E1ln\u00EDch \u0161k\u016Fdc\u016F a terorist\u016F)."@cs ,
"\u0414\u043E\u0431\u0443\u0432\u0430\u0301\u043D\u043D\u044F \u0434\u0430\u0301\u043D\u0438\u0445 \u2014 \u0432\u0438\u044F\u0432\u043B\u0435\u043D\u043D\u044F \u043F\u0440\u0438\u0445\u043E\u0432\u0430\u043D\u0438\u0445 \u0437\u0430\u043A\u043E\u043D\u043E\u043C\u0456\u0440\u043D\u043E\u0441\u0442\u0435\u0439 \u0430\u0431\u043E \u0432\u0437\u0430\u0454\u043C\u043E\u0437\u0432'\u044F\u0437\u043A\u0456\u0432 \u043C\u0456\u0436 \u0437\u043C\u0456\u043D\u043D\u0438\u043C\u0438 \u0443 \u0432\u0435\u043B\u0438\u043A\u0438\u0445 \u043C\u0430\u0441\u0438\u0432\u0430\u0445 \u043D\u0435\u043E\u0431\u0440\u043E\u0431\u043B\u0435\u043D\u0438\u0445 \u0434\u0430\u043D\u0438\u0445. \u042F\u043A \u043F\u0440\u0430\u0432\u0438\u043B\u043E \u043F\u043E\u0434\u0456\u043B\u044F\u0454\u0442\u044C\u0441\u044F \u043D\u0430 \u0437\u0430\u0434\u0430\u0447\u0456 \u043A\u043B\u0430\u0441\u0438\u0444\u0456\u043A\u0430\u0446\u0456\u0457, \u043C\u043E\u0434\u0435\u043B\u044E\u0432\u0430\u043D\u043D\u044F \u0442\u0430 \u043F\u0440\u043E\u0433\u043D\u043E\u0437\u0443\u0432\u0430\u043D\u043D\u044F. \u041D\u0430 \u0441\u0443\u0447\u0430\u0441\u043D\u0438\u0445 \u043F\u0456\u0434\u043F\u0440\u0438\u0454\u043C\u0441\u0442\u0432\u0430\u0445, \u0432 \u0434\u043E\u0441\u043B\u0456\u0434\u043D\u0438\u0446\u044C\u043A\u0438\u0445 \u043F\u0440\u043E\u0435\u043A\u0442\u0430\u0445 \u0430\u0431\u043E \u0432 \u0456\u043D\u0442\u0435\u0440\u043D\u0435\u0442\u0456 \u0443\u0442\u0432\u043E\u0440\u044E\u044E\u0442\u044C\u0441\u044F \u0432\u0435\u043B\u0438\u043A\u0456 \u043E\u0431\u0441\u044F\u0433\u0438 \u0434\u0430\u043D\u0438\u0445. \u0414\u043E\u0431\u0443\u0432\u0430\u043D\u043D\u044F \u0434\u0430\u043D\u0438\u0445 \u0434\u0430\u0454 \u043C\u043E\u0436\u043B\u0438\u0432\u0456\u0441\u0442\u044C \u0430\u0432\u0442\u043E\u043C\u0430\u0442\u0438\u0447\u043D\u043E\u0433\u043E \u0430\u043D\u0430\u043B\u0456\u0437\u0443 \u0446\u0438\u0445 \u0434\u0430\u043D\u0438\u0445 \u0448\u043B\u044F\u0445\u043E\u043C \u0437\u0430\u0441\u0442\u043E\u0441\u0443\u0432\u0430\u043D\u043D\u044F \u043C\u0435\u0442\u043E\u0434\u0456\u0432 \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u0447\u043D\u043E\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043A\u0438, \u0448\u0442\u0443\u0447\u043D\u0438\u0445 \u043D\u0435\u0439\u0440\u043E\u043D\u043D\u0438\u0445 \u043C\u0435\u0440\u0435\u0436, \u0442\u0435\u043E\u0440\u0456\u0457 \u043D\u0435\u0447\u0456\u0442\u043A\u0438\u0445 \u043C\u043D\u043E\u0436\u0438\u043D \u0430\u0431\u043E \u0433\u0435\u043D\u0435\u0442\u0438\u0447\u043D\u0438\u0445 \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C\u0456\u0432. \u041C\u0435\u0442\u043E\u044E \u0430\u043D\u0430\u043B\u0456\u0437\u0443 \u0454 \u0432\u0438\u044F\u0432\u043B\u0435\u043D\u043D\u044F \u043F\u0440\u0430\u0432\u0438\u043B \u0442\u0430 \u0437\u0430\u043A\u043E\u043D\u043E\u043C\u0456\u0440\u043D\u043E\u0441\u0442\u0435\u0439, \u043D\u0430\u043F\u0440\u0438\u043A\u043B\u0430\u0434, \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043D\u0438\u0445 \u043F\u043E\u0434\u0456\u0439. \u0422\u0430\u043A, \u043D\u0430\u043F\u0440\u0438\u043A\u043B\u0430\u0434, \u043C\u043E\u0436\u0443\u0442\u044C \u0432\u0438\u044F\u0432\u043B\u044F\u0442\u0438\u0441\u044C \u0437\u043C\u0456\u043D\u0438 \u0443 \u043F\u043E\u0432\u0435\u0434\u0456\u043D\u0446\u0456 \u043A\u043B\u0456\u0454\u043D\u0442\u0456\u0432 \u0430\u0431\u043E \u0433\u0440\u0443\u043F \u043A\u043B\u0456\u0454\u043D\u0442\u0456\u0432 \u0434\u043B\u044F \u043F\u043E\u043A\u0440\u0430\u0449\u0435\u043D\u043D\u044F \u043F\u043E\u043B\u0456\u0442\u0438\u043A\u0438 \u043F\u0456\u0434\u043F\u0440\u0438\u0454\u043C\u0441\u0442\u0432\u0430."@uk ,
"\u6570\u636E\u6316\u6398\uFF08\u82F1\u8A9E\uFF1AData mining\uFF09\uFF0C\u53C8\u8B6F\u70BA\u8CC7\u6599\u63A1\u7926\u3001\u8CC7\u6599\u63A2\u52D8\u3002\u5B83\u662F\u8CC7\u6599\u5EAB\u77E5\u8B58\u767C\u73FE\uFF08\u82F1\u8A9E\uFF1AKnowledge-Discovery in Databases\uFF0C\u7C21\u7A31\uFF1AKDD)\u4E2D\u7684\u4E00\u500B\u6B65\u9A5F\u3002\u6570\u636E\u6316\u6398\u4E00\u822C\u662F\u6307\u5F9E\u5927\u91CF\u7684\u8CC7\u6599\u4E2D\u81EA\u52D5\u641C\u7D22\u96B1\u85CF\u65BC\u5176\u4E2D\u7684\u6709\u7740\u7279\u6B8A\u95DC\u806F\u6027\uFF08\u5C6C\u65BCAssociation rule learning\uFF09\u7684\u4FE1\u606F\u7684\u904E\u7A0B\u3002\u8CC7\u6599\u6316\u6398\u901A\u5E38\u8207\u96FB\u8166\u79D1\u5B78\u6709\u95DC\uFF0C\u4E26\u901A\u904E\u7D71\u8A08\u3001\u5728\u7EBF\u5206\u6790\u5904\u7406\u3001\u60C5\u5831\u6AA2\u7D22\u3001\u6A5F\u5668\u5B78\u7FD2\u3001\u5C08\u5BB6\u7CFB\u7D71\uFF08\u4F9D\u9760\u904E\u53BB\u7684\u7D93\u9A57\u6CD5\u5247\uFF09\u548C\u6A21\u5F0F\u8B58\u5225\u7B49\u8AF8\u591A\u65B9\u6CD5\u4F86\u5BE6\u73FE\u4E0A\u8FF0\u76EE\u6A19\u3002"@zh ,
"Prospec\u00E7\u00E3o de dados ou minera\u00E7\u00E3o de dados (tamb\u00E9m conhecida pelo termo ingl\u00EAs data mining) \u00E9 o processo de explorar grandes quantidades de dados \u00E0 procura de padr\u00F5es consistentes, como regras de associa\u00E7\u00E3o ou sequ\u00EAncias temporais, para detectar relacionamentos sistem\u00E1ticos entre vari\u00E1veis, detectando assim novos subconjuntos de dados. Esse \u00E9 um t\u00F3pico recente em ci\u00EAncia da computa\u00E7\u00E3o mas utiliza v\u00E1rias t\u00E9cnicas da estat\u00EDstica, recupera\u00E7\u00E3o de informa\u00E7\u00E3o, intelig\u00EAncia artificial e reconhecimento de padr\u00F5es."@pt ,
"\u0418\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0443\u0430\u043B\u044C\u043D\u044B\u0439 \u0430\u043D\u0430\u043B\u0438\u0437 \u0434\u0430\u043D\u043D\u044B\u0445 \u2014 \u0432\u044B\u044F\u0432\u043B\u0435\u043D\u0438\u0435 \u0441\u043A\u0440\u044B\u0442\u044B\u0445 \u0437\u0430\u043A\u043E\u043D\u043E\u043C\u0435\u0440\u043D\u043E\u0441\u0442\u0435\u0439 \u0438\u043B\u0438 \u0432\u0437\u0430\u0438\u043C\u043E\u0441\u0432\u044F\u0437\u0435\u0439 \u043C\u0435\u0436\u0434\u0443 \u043F\u0435\u0440\u0435\u043C\u0435\u043D\u043D\u044B\u043C\u0438 \u0432 \u0431\u043E\u043B\u044C\u0448\u0438\u0445 \u043C\u0430\u0441\u0441\u0438\u0432\u0430\u0445 \u043D\u0435\u043E\u0431\u0440\u0430\u0431\u043E\u0442\u0430\u043D\u043D\u044B\u0445 \u0434\u0430\u043D\u043D\u044B\u0445. \u041F\u043E\u0434\u0440\u0430\u0437\u0434\u0435\u043B\u044F\u0435\u0442\u0441\u044F \u043D\u0430 \u0437\u0430\u0434\u0430\u0447\u0438 \u043A\u043B\u0430\u0441\u0441\u0438\u0444\u0438\u043A\u0430\u0446\u0438\u0438, \u043C\u043E\u0434\u0435\u043B\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0438 \u043F\u0440\u043E\u0433\u043D\u043E\u0437\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0438 \u0434\u0440\u0443\u0433\u0438\u0435. \u0422\u0435\u0440\u043C\u0438\u043D \u00ABData Mining\u00BB \u0432\u0432\u0435\u0434\u0435\u043D \u0413\u0440\u0438\u0433\u043E\u0440\u0438\u0435\u043C \u041F\u044F\u0442\u0435\u0446\u043A\u0438\u043C-\u0428\u0430\u043F\u0438\u0440\u043E \u0432 1989 \u0433\u043E\u0434\u0443. \u0410\u043D\u0433\u043B\u0438\u0439\u0441\u043A\u0438\u0439 \u0442\u0435\u0440\u043C\u0438\u043D \u00ABData Mining\u00BB \u043D\u0435 \u0438\u043C\u0435\u0435\u0442 \u043E\u0434\u043D\u043E\u0437\u043D\u0430\u0447\u043D\u043E\u0433\u043E \u043F\u0435\u0440\u0435\u0432\u043E\u0434\u0430 \u043D\u0430 \u0440\u0443\u0441\u0441\u043A\u0438\u0439 \u044F\u0437\u044B\u043A (\u0434\u043E\u0431\u044B\u0447\u0430 \u0434\u0430\u043D\u043D\u044B\u0445, \u0432\u0441\u043A\u0440\u044B\u0442\u0438\u0435 \u0434\u0430\u043D\u043D\u044B\u0445, \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u043E\u043D\u043D\u0430\u044F \u043F\u0440\u043E\u0445\u043E\u0434\u043A\u0430, \u0438\u0437\u0432\u043B\u0435\u0447\u0435\u043D\u0438\u0435 \u0434\u0430\u043D\u043D\u044B\u0445/\u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u0438) \u043F\u043E\u044D\u0442\u043E\u043C\u0443 \u0432 \u0431\u043E\u043B\u044C\u0448\u0438\u043D\u0441\u0442\u0432\u0435 \u0441\u043B\u0443\u0447\u0430\u0435\u0432 \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u0435\u0442\u0441\u044F \u0432 \u043E\u0440\u0438\u0433\u0438\u043D\u0430\u043B\u0435. \u041D\u0430\u0438\u0431\u043E\u043B\u0435\u0435 \u0443\u0434\u0430\u0447\u043D\u044B\u043C \u043D\u0435\u043F\u0440\u044F\u043C\u044B\u043C \u043F\u0435\u0440\u0435\u0432\u043E\u0434\u043E\u043C \u0441\u0447\u0438\u0442\u0430\u0435\u0442\u0441\u044F \u0442\u0435\u0440\u043C\u0438\u043D \u00AB\u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0443\u0430\u043B\u044C\u043D\u044B\u0439 \u0430\u043D\u0430\u043B\u0438\u0437 \u0434\u0430\u043D\u043D\u044B\u0445\u00BB (\u0418\u0410\u0414). \u0418\u0410\u0414 \u0432\u043A\u043B\u044E\u0447\u0430\u0435\u0442 \u043C\u0435\u0442\u043E\u0434\u044B \u0438 \u043C\u043E\u0434\u0435\u043B\u0438 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0433\u043E \u0430\u043D\u0430\u043B\u0438\u0437\u0430 \u0438 \u043C\u0430\u0448\u0438\u043D\u043D\u043E\u0433\u043E \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u044F, \u0434\u0438\u0441\u0442\u0430\u043D\u0446\u0438\u0440\u0443\u044F\u0441\u044C \u043E\u0442 \u043D\u0438\u0445 \u0432 \u0441\u0442\u043E\u0440\u043E\u043D\u0443 \u0430\u0432\u0442\u043E\u043C\u0430\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0433\u043E \u0430\u043D\u0430\u043B\u0438\u0437\u0430 \u0434\u0430\u043D\u043D\u044B\u0445. \u0418\u043D\u0441\u0442\u0440\u0443\u043C\u0435\u043D\u0442\u044B \u0418\u0410\u0414 \u043F\u043E\u0437\u0432\u043E\u043B\u044F\u044E\u0442 \u043F\u0440\u043E\u0432\u043E\u0434\u0438\u0442\u044C \u0430\u043D\u0430\u043B\u0438\u0437 \u0434\u0430\u043D\u043D\u044B\u0445 \u043F\u0440\u0435\u0434\u043C\u0435\u0442\u043D\u044B\u043C\u0438 \u0441\u043F\u0435\u0446\u0438\u0430\u043B\u0438\u0441\u0442\u0430\u043C\u0438 (\u0430\u043D\u0430\u043B\u0438\u0442\u0438\u043A\u0430\u043C\u0438), \u043D\u0435 \u0432\u043B\u0430\u0434\u0435\u044E\u0449\u0438\u043C\u0438 \u0441\u043E\u043E\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044E\u0449\u0438\u043C\u0438 \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u043C\u0438 \u0437\u043D\u0430\u043D\u0438\u044F\u043C\u0438."@ru ,
"Data mining is the process of extracting patterns from data. As more data are gathered, with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform these data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery. While data mining can be used to uncover patterns in data samples, it is important to be aware that the use of non-representative samples of data may produce results that are not indicative of the domain. Similarly, data mining will not find patterns that may be present in the domain, if those patterns are not present in the sample being \"mined\". There is a tendency for insufficiently knowledgeable \"consumers\" of the results to attribute \"magical abilities\" to data mining, treating the technique as a sort of all-seeing crystal ball. Like any other tool, it only functions in conjunction with the appropriate raw material: in this case, indicative and representative data that the user must first collect. Further, the discovery of a particular pattern in a particular set of data does not necessarily mean that pattern is representative of the whole population from which that data was drawn. Hence, an important part of the process is the verification and validation of patterns on other samples of data. The term data mining has also been used in a related but negative sense, to mean the deliberate searching for apparent but not necessarily representative patterns in large numbers of data. To avoid confusion with the other sense, the terms data dredging and data snooping are often used. Note, however, that dredging and snooping can be (and sometimes are) used as exploratory tools when developing and clarifying hypotheses."@en ,
"Eksploracja danych (spotyka si\u0119 r\u00F3wnie\u017C okre\u015Blenie dr\u0105\u017Cenie danych, pozyskiwanie wiedzy, wydobywanie danych, ekstrakcja danych) to jeden z etap\u00F3w procesu odkrywania wiedzy z baz danych (ang. Knowledge Discovery in Databases, KDD). Istnieje wiele technik eksploracji danych, kt\u00F3re wywodz\u0105 si\u0119 z ugruntowanych dziedzin nauki takich jak statystyka (statystyczna analiza wielowymiarowa) i uczenie maszynowe. Idea eksploracji danych polega na wykorzystaniu szybko\u015Bci komputera do znajdowania ukrytych dla cz\u0142owieka (w\u0142a\u015Bnie z uwagi na ograniczone mo\u017Cliwo\u015Bci czasowe) prawid\u0142owo\u015Bci w danych zgromadzonych w hurtowniach danych."@pl ,
"Tiedonlouhinta tarkoittaa joukkoa menetelmi\u00E4, joilla pyrit\u00E4\u00E4n oleellisen l\u00F6yt\u00E4miseen suurista tietomassoista."@fi ,
"La mineria de dades (Data Mining) \u00E9s un proc\u00E9s no trivial d'identificaci\u00F3 v\u00E0lida, nova, potencialment \u00FAtil i entendible de patrons comprensibles que es troben ocults en les dades (Fayyad i altres, 1996). Les bases de la mineria de dades es troben en la intel\u00B7lig\u00E8ncia artificial i en l'an\u00E0lisi estad\u00EDstica. Mitjan\u00E7ant els models extrets utilitzant t\u00E8cniques de mineria de dades s'aborda la soluci\u00F3 a problemes de predicci\u00F3, classificaci\u00F3 i segmentaci\u00F3."@ca ,
"Datamining is het op een geautomatiseerde manier patronen en relaties ontdekken in grote hoeveelheden gegevens. De naam komt voort aan de overeenkomsten tussen het zoeken naar waardevolle bedrijfsinformatie en het graven (mining) naar iets waardevols in een grote berg. Datamining is gebaseerd op statistiek, machine learning, patroonherkenning, database management en geavanceerde computerberekeningen. Het wordt vaak toegepast op een datawarehouse. Het kan nieuwe informatie opleveren, die zonder de techniek niet gevonden zou zijn. Het geautomatiseerd verwerken van grote hoeveelheden persoonsgegevens kan echter stuiten op barri\u00E8res met betrekking tot privacy, legaliteit en ethiek. Data mining maakt een onderdeel uit van een meer omvattend proces dat doorgaans wordt aangeduid als business intelligence. Data mining wordt vaak toegepast op grote hoeveelheden biologische, chemische en medische data. Deze techniek wordt dan met name toegepast op microarraydata of prote\u00EFnenmicroarraydata. Dit zijn onderzoeksgebieden binnen de de bio-informatica. Data miners kunnen gebruik maken van de volgende technieken: Case Based Redeneren : deze benadering gebruikt cases uit het verleden om er bepaalde patronen in te herkennen. Neuraal Computing: deze benadering onderzoekt historische data voor het herkennen van bepaalde patronen. Intelligente Agenten: hierbij wordt informatie van het internet gehaald en van databases die op intranet gebaseerd zijn. Samengaan Analyses: hierbij wordt gebruikt gemakt van gespecialiseerde algoritmen die grote hoeveelheden data uitzoeken en statistische regels voor bepaalde onderdelen uiten. Een bekend algoritme voor data mining is het a priori algoritme van Rakesh Agrawal et al. Een andere methode is ComPair. Verschillende Applicaties van DataMining: Bij de Detailhandel wordt het gebruikt voor het voorspellen van verkopen, bepalen van correcte voorraadniveaus en distributieschema's voor winkels. Bij Bankieren wordt het gebruikt voor het voorspellen van het niveau van slechte leningen en bedrieglijke creditcardhouders en welke klanten het beste reageren op nieuwe lening aanbiedingen. Bij Productie wordt het gebruikt voor het voorspellen van machinedefecten en het vinden van factoren die de optimalisatie van productiecapaciteit beheersen. Bij Verzekering wordt het gebruikt voor het voorspellen van de kosten van claims en het voorspellen welke klanten welke verzekeringspolis kopen. Bij Politie wordt het gebruikt voor het volgen van patronen, locaties en gedrag in de criminaliteit en het identificeren van attributen die kunnen assisteren bij het oplossen van criminaliteitszaken"@nl ,
"\u30C7\u30FC\u30BF\u30DE\u30A4\u30CB\u30F3\u30B0\uFF08Data mining\uFF09\u3068\u306F\u3001\u7D71\u8A08\u5B66\u3001\u30D1\u30BF\u30FC\u30F3\u8A8D\u8B58\u3001\u4EBA\u5DE5\u77E5\u80FD\u7B49\u306E\u30C7\u30FC\u30BF\u89E3\u6790\u306E\u6280\u6CD5\u3092\u5927\u91CF\u306E\u30C7\u30FC\u30BF\u306B\u7DB2\u7F85\u7684\u306B\u9069\u7528\u3059\u308B\u3053\u3068\u3067\u77E5\u8B58\u3092\u53D6\u308A\u51FA\u3059\u6280\u8853\u3002DM\u3068\u7565\u3057\u3066\u547C\u3070\u308C\u308B\u4E8B\u3082\u3042\u308B\u3002\u901A\u5E38\u306E\u30C7\u30FC\u30BF\u306E\u6271\u3044\u65B9\u304B\u3089\u306F\u60F3\u50CF\u304C\u53CA\u3073\u306B\u304F\u3044\u3001\u767A\u898B\u7684\uFF08heuristic\uFF09\u306A\u77E5\u8B58\u7372\u5F97\u304C\u53EF\u80FD\u3067\u3042\u308B\u3068\u3044\u3046\u671F\u5F85\u3092\u542B\u610F\u3057\u3066\u3044\u308B\u3053\u3068\u304C\u591A\u3044\u3002\u82F1\u8A9E\u3067\u306Fknowledge-discovery in databases\uFF08\u30C7\u30FC\u30BF\u30D9\u30FC\u30B9\u304B\u3089\u306E\u77E5\u8B58\u767A\u898B\uFF09\u306E\u982D\u6587\u5B57\u3092\u3068\u3063\u3066KDD\u3068\u3082\u547C\u3070\u308C\u308B\u3002"@ja ,
"Data mining (norsk datagraving, dataminering, datautvinning) er et fag innen informatikk, der man studerer leting etter struktur og ofte mening, i ofte store mengder med ustrukturerte data. Sentrale teknikker som brukes er m\u00F8nstergjenkjenning, statistisk modellering og maskinl\u00E6ring. S\u00F8k i ustrukturerte data har v\u00E6rt et eget fag i informatikken lenge, men begrepet data mining kom spesielt etter at man kunne fylle s\u00E5kalte datavarehus med data av alle slag. En kunne da mot en pengesum, f\u00E5 tilgang til de innsamlede data, for s\u00E5 \u00E5 iverksette s\u00F8king og graving etter noe som for klienten var av interesse."@no ,
"Informationsutvinning, \u00E4ven kallat Data mining betecknar s\u00F6kandet efter m\u00F6nster i stora datam\u00E4ngder. Begreppet har vuxit fram sedan v\u00E4xande databasers storlek har begr\u00E4nsat m\u00F6jligheterna till kompletta statistiska analyser inom omr\u00E5den som biologi och finansiella kalkyler. Genom exempelvis algoritmer eller manuell observation f\u00F6rs\u00F6ker man finna relationer mellan datapunkterna, f\u00F6r att b\u00E4ttre kunna visualisera eller utnyttja den komplexa informationen. Informationsutvinning kan anv\u00E4nda sig av olika tekniker, bl.a. m\u00F6nsterigenk\u00E4nning och subjektbaserad igenk\u00E4nning."@sv ,
"Az adatb\u00E1ny\u00E1szat a nagymennyis\u00E9g\u0171 adatokban rejl\u0151 inform\u00E1ci\u00F3k f\u00E9l-automatikus felt\u00E1r\u00E1sa mesters\u00E9ges intelligencia algoritmusok alkalmaz\u00E1s\u00E1val (p\u00E9ld\u00E1ul neur\u00E1lis h\u00E1l\u00F3k, szab\u00E1lygener\u00E1l\u00F3k, asszoci\u00E1ci\u00F3s modellek). A k\u00F6znyelv \u00E9s k\u00FCl\u00F6nb\u00F6z\u0151 informatikai c\u00E9gek sok mindent neveznek adatb\u00E1ny\u00E1sz\u00E1s\u00E1nak, de a szigor\u00FAbb szakmai terminol\u00F3gia szerint nem tekinthet\u0151 adatb\u00E1ny\u00E1szatnak az adatokb\u00F3l lek\u00E9rdez\u00E9sekkel, aggreg\u00E1l\u00E1sokkal, illetve alap-statisztikai vizsg\u00E1latokkal t\u00F6rt\u00E9n\u0151 inform\u00E1ci\u00F3 nyer\u00E9s."@hu ;
rdfs:comment "Il data mining ha per oggetto l'estrazione di un sapere o di una conoscenza a partire da grandi quantit\u00E0 di dati (attraverso metodi automatici o semi-automatici) e l'utilizzazione industriale o operativa di questo sapere."@it ,
""@ja ,
"Informationsutvinning, \u00E4ven kallat Data mining betecknar s\u00F6kandet efter m\u00F6nster i stora datam\u00E4ngder. Begreppet har vuxit fram sedan v\u00E4xande databasers storlek har begr\u00E4nsat m\u00F6jligheterna till kompletta statistiska analyser inom omr\u00E5den som biologi och finansiella kalkyler. Genom exempelvis algoritmer eller manuell observation f\u00F6rs\u00F6ker man finna relationer mellan datapunkterna, f\u00F6r att b\u00E4ttre kunna visualisera eller utnyttja den komplexa informationen."@sv ,
"\u0414\u043E\u0431\u0443\u0432\u0430\u0301\u043D\u043D\u044F \u0434\u0430\u0301\u043D\u0438\u0445 \u2014 \u0432\u0438\u044F\u0432\u043B\u0435\u043D\u043D\u044F \u043F\u0440\u0438\u0445\u043E\u0432\u0430\u043D\u0438\u0445 \u0437\u0430\u043A\u043E\u043D\u043E\u043C\u0456\u0440\u043D\u043E\u0441\u0442\u0435\u0439 \u0430\u0431\u043E \u0432\u0437\u0430\u0454\u043C\u043E\u0437\u0432'\u044F\u0437\u043A\u0456\u0432 \u043C\u0456\u0436 \u0437\u043C\u0456\u043D\u043D\u0438\u043C\u0438 \u0443 \u0432\u0435\u043B\u0438\u043A\u0438\u0445 \u043C\u0430\u0441\u0438\u0432\u0430\u0445 \u043D\u0435\u043E\u0431\u0440\u043E\u0431\u043B\u0435\u043D\u0438\u0445 \u0434\u0430\u043D\u0438\u0445. \u042F\u043A \u043F\u0440\u0430\u0432\u0438\u043B\u043E \u043F\u043E\u0434\u0456\u043B\u044F\u0454\u0442\u044C\u0441\u044F \u043D\u0430 \u0437\u0430\u0434\u0430\u0447\u0456 \u043A\u043B\u0430\u0441\u0438\u0444\u0456\u043A\u0430\u0446\u0456\u0457, \u043C\u043E\u0434\u0435\u043B\u044E\u0432\u0430\u043D\u043D\u044F \u0442\u0430 \u043F\u0440\u043E\u0433\u043D\u043E\u0437\u0443\u0432\u0430\u043D\u043D\u044F."@uk ,
"Data mining (norsk datagraving, dataminering, datautvinning) er et fag innen informatikk, der man studerer leting etter struktur og ofte mening, i ofte store mengder med ustrukturerte data. Sentrale teknikker som brukes er m\u00F8nstergjenkjenning, statistisk modellering og maskinl\u00E6ring. S\u00F8k i ustrukturerte data har v\u00E6rt et eget fag i informatikken lenge, men begrepet data mining kom spesielt etter at man kunne fylle s\u00E5kalte datavarehus med data av alle slag."@no ,
"Prospec\u00E7\u00E3o de dados ou minera\u00E7\u00E3o de dados (tamb\u00E9m conhecida pelo termo ingl\u00EAs data mining) \u00E9 o processo de explorar grandes quantidades de dados \u00E0 procura de padr\u00F5es consistentes, como regras de associa\u00E7\u00E3o ou sequ\u00EAncias temporais, para detectar relacionamentos sistem\u00E1ticos entre vari\u00E1veis, detectando assim novos subconjuntos de dados."@pt ,
"\u0418\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0443\u0430\u043B\u044C\u043D\u044B\u0439 \u0430\u043D\u0430\u043B\u0438\u0437 \u0434\u0430\u043D\u043D\u044B\u0445 \u2014 \u0432\u044B\u044F\u0432\u043B\u0435\u043D\u0438\u0435 \u0441\u043A\u0440\u044B\u0442\u044B\u0445 \u0437\u0430\u043A\u043E\u043D\u043E\u043C\u0435\u0440\u043D\u043E\u0441\u0442\u0435\u0439 \u0438\u043B\u0438 \u0432\u0437\u0430\u0438\u043C\u043E\u0441\u0432\u044F\u0437\u0435\u0439 \u043C\u0435\u0436\u0434\u0443 \u043F\u0435\u0440\u0435\u043C\u0435\u043D\u043D\u044B\u043C\u0438 \u0432 \u0431\u043E\u043B\u044C\u0448\u0438\u0445 \u043C\u0430\u0441\u0441\u0438\u0432\u0430\u0445 \u043D\u0435\u043E\u0431\u0440\u0430\u0431\u043E\u0442\u0430\u043D\u043D\u044B\u0445 \u0434\u0430\u043D\u043D\u044B\u0445. \u041F\u043E\u0434\u0440\u0430\u0437\u0434\u0435\u043B\u044F\u0435\u0442\u0441\u044F \u043D\u0430 \u0437\u0430\u0434\u0430\u0447\u0438 \u043A\u043B\u0430\u0441\u0441\u0438\u0444\u0438\u043A\u0430\u0446\u0438\u0438, \u043C\u043E\u0434\u0435\u043B\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0438 \u043F\u0440\u043E\u0433\u043D\u043E\u0437\u0438\u0440\u043E\u0432\u0430\u043D\u0438\u044F \u0438 \u0434\u0440\u0443\u0433\u0438\u0435."@ru ,
"Data mining (angl. dolov\u00E1n\u00ED z dat \u010Di vyt\u011B\u017Eov\u00E1n\u00ED dat) je analytick\u00E1 metodologie z\u00EDsk\u00E1v\u00E1n\u00ED netrivi\u00E1ln\u00EDch skryt\u00FDch a potenci\u00E1ln\u011B u\u017Eite\u010Dn\u00FDch informac\u00ED z dat. N\u011Bkdy se ch\u00E1pe jako analytick\u00E1 sou\u010D\u00E1st dob\u00FDv\u00E1n\u00ED znalost\u00ED z datab\u00E1z\u00ED (Knowledge Discovery in Databases, KDD), tak nap\u0159\u00EDklad Berka (2003), jindy se tato dv\u011B ozna\u010Den\u00ED ch\u00E1pou jako souzna\u010Dn\u00E1."@cs ,
"La miner\u00EDa de datos (DM, Data Mining) consiste en la extracci\u00F3n no trivial de informaci\u00F3n que reside de manera impl\u00EDcita en los datos. Dicha informaci\u00F3n era previamente desconocida y podr\u00E1 resultar \u00FAtil para alg\u00FAn proceso. En otras palabras, la miner\u00EDa de datos prepara, sondea y explora los datos para sacar la informaci\u00F3n oculta en ellos."@es ,
"Exploatarea datelor (adesea definit\u0103 prin termenul englez Data mining) este un proces de sortare a unor cantit\u0103\u0163i mari de date \u015Fi de extragere a informa\u0163iilor relevante din acestea."@ro ,
"Datamining is het op een geautomatiseerde manier patronen en relaties ontdekken in grote hoeveelheden gegevens. De naam komt voort aan de overeenkomsten tussen het zoeken naar waardevolle bedrijfsinformatie en het graven (mining) naar iets waardevols in een grote berg. Datamining is gebaseerd op statistiek, machine learning, patroonherkenning, database management en geavanceerde computerberekeningen. Het wordt vaak toegepast op een datawarehouse."@nl ,
"Eksploracja danych (spotyka si\u0119 r\u00F3wnie\u017C okre\u015Blenie dr\u0105\u017Cenie danych, pozyskiwanie wiedzy, wydobywanie danych, ekstrakcja danych) to jeden z etap\u00F3w procesu odkrywania wiedzy z baz danych (ang. Knowledge Discovery in Databases, KDD). Istnieje wiele technik eksploracji danych, kt\u00F3re wywodz\u0105 si\u0119 z ugruntowanych dziedzin nauki takich jak statystyka (statystyczna analiza wielowymiarowa) i uczenie maszynowe."@pl ,
""@zh ,
"Az adatb\u00E1ny\u00E1szat a nagymennyis\u00E9g\u0171 adatokban rejl\u0151 inform\u00E1ci\u00F3k f\u00E9l-automatikus felt\u00E1r\u00E1sa mesters\u00E9ges intelligencia algoritmusok alkalmaz\u00E1s\u00E1val (p\u00E9ld\u00E1ul neur\u00E1lis h\u00E1l\u00F3k, szab\u00E1lygener\u00E1l\u00F3k, asszoci\u00E1ci\u00F3s modellek)."@hu ,
"Basit bir tan\u0131m yapmak gerekirse veri madencili\u011Fi, b\u00FCy\u00FCk \u00F6l\u00E7ekli veriler aras\u0131ndan bilgiye ula\u015Fma, bilgiyi madenleme i\u015Fidir. Ya da bir anlamda b\u00FCy\u00FCk veri y\u0131\u011F\u0131nlar\u0131 i\u00E7erisinden gelecekle ilgili tahminde bulunabilmemizi sa\u011Flayabilecek ba\u011F\u0131nt\u0131lar\u0131n bilgisayar program\u0131 kullanarak aranmas\u0131d\u0131r. Veri madencili\u011Fi deyimi yanl\u0131\u015F kullan\u0131lan bir deyim olabilece\u011Finden buna e\u015F de\u011Fer ba\u015Fka kullan\u0131mlar da literat\u00FCre ge\u00E7mi\u015Ftir. Veritabanlar\u0131nda bilgi madencili\u011Fi (\u0130ng."@tr ,
"La mineria de dades (Data Mining) \u00E9s un proc\u00E9s no trivial d'identificaci\u00F3 v\u00E0lida, nova, potencialment \u00FAtil i entendible de patrons comprensibles que es troben ocults en les dades (Fayyad i altres, 1996). Les bases de la mineria de dades es troben en la intel\u00B7lig\u00E8ncia artificial i en l'an\u00E0lisi estad\u00EDstica. Mitjan\u00E7ant els models extrets utilitzant t\u00E8cniques de mineria de dades s'aborda la soluci\u00F3 a problemes de predicci\u00F3, classificaci\u00F3 i segmentaci\u00F3."@ca ,
"Data mining is the process of extracting patterns from data. As more data are gathered, with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform these data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery."@en ,
"Tiedonlouhinta tarkoittaa joukkoa menetelmi\u00E4, joilla pyrit\u00E4\u00E4n oleellisen l\u00F6yt\u00E4miseen suurista tietomassoista."@fi ,
"L\u2019exploration de donn\u00E9es, aussi connue sous les noms fouille de donn\u00E9es, data mining (forage de donn\u00E9es) ou encore Extraction de Connaissances \u00E0 partir de Donn\u00E9es (ECD en fran\u00E7ais, KDD en Anglais), a pour objet l\u2019extraction d'un savoir ou d'une connaissance \u00E0 partir de grandes quantit\u00E9s de donn\u00E9es, par des m\u00E9thodes automatiques ou semi-automatiques, et l'utilisation industrielle ou op\u00E9rationnelle de ce savoir."@fr ,
"Unter Data Mining (englisch f\u00FCr \u201EDatensch\u00FCrfen\u201C) versteht man die systematische Anwendung von Methoden, die meist statistisch-mathematisch begr\u00FCndet sind, auf einen Datenbestand mit dem Ziel der Mustererkennung. Hierbei geht es vor allem um das Durchsuchen sehr gro\u00DFer Datenbest\u00E4nde, weswegen vor allem solche Methoden betrachtet werden, die eine hervorragende asymptotische Laufzeit haben."@de .
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