{"id":97890,"date":"2020-10-22T20:43:19","date_gmt":"2020-10-22T18:43:19","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/razbiraemsya-v-chem-raznicza-mezhdu-data-mining-i-data-extraction"},"modified":"2021-02-01T11:42:45","modified_gmt":"2021-02-01T09:42:45","slug":"razbiraemsya-v-chem-raznicza-mezhdu-data-mining-i-data-extraction","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/administrirovanie\/razbiraemsya-v-chem-raznicza-mezhdu-data-mining-i-data-extraction","title":{"rendered":"S\u0103 \u00een\u021belegem care este diferen\u021ba dintre Data Mining \u0219i Data Extraction","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/skillfactory\/blog\/524336\/\"><img decoding=\"async\" alt=\"S\u0103 \u00een\u021belegem care este diferen\u021ba dintre Data Mining \u0219i Data Extraction\" src=\"\/wp-content\/uploads\/2020\/10\/bd0e140b564b7f6696b346865a91784f.png\" style=\"display:block;margin: 0 auto;\"><\/a><\/noindex><br \/>\nCele dou\u0103 cuvinte la mod\u0103 asociate cu Data Science \u00eei confund\u0103 pe mul\u021bi oameni. Data Mining este adesea \u00een\u021beles gre\u0219it ca fiind extragerea \u0219i ob\u021binerea de date, dar de fapt, lucrurile sunt mult mai complexe. \u00cen aceast\u0103 postare, s\u0103 clarific\u0103m ce \u00eenseamn\u0103 Mining \u0219i s\u0103 vedem care este diferen\u021ba dintre Data Mining \u0219i Data Extraction.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Ce este Data Mining?<\/h2>\n<p>Data mining, de asemenea, denumit <strong>Descoperirea cuno\u0219tin\u021belor \u00een baze de date (KDD)<\/strong>, reprezint\u0103 o metod\u0103 adesea utilizat\u0103 pentru analiza unor cantit\u0103\u021bi mari de date prin metode statistice \u0219i matematice pentru a g\u0103si modele sau tendin\u021be ascunse \u0219i pentru a extrage valoare din acestea.<\/p>\n<h2>Ce se poate face cu ajutorul Data Mining?<\/h2>\n<p>Automatiz\u00e2nd procesul, <noindex><a rel=\"nofollow\" href=\"https:\/\/www.octoparse.com\/blog\/7-web-mining-tools-around-the-web\">instrumentele de data mining<\/a><\/noindex> pot scana bazele de date \u0219i pot identifica eficient tipare ascunse. Pentru \u00eentreprinderi, data mining este adesea utilizat pentru a descoperi corela\u021bii \u0219i rela\u021bii \u00een date, ajut\u00e2nd la luarea deciziilor optime \u00een afaceri.<\/p>\n<h2>Exemple de aplicare<\/h2>\n<p>Dup\u0103 ce data mining a c\u00e2\u0219tigat popularitate \u00een anii '90, companii dintr-o varietate de domenii, inclusiv comer\u021b cu am\u0103nuntul, finan\u021be, s\u0103n\u0103tate, transporturi, telecomunica\u021bii, comer\u021b electronic etc., au \u00eenceput s\u0103 utilizeze metodele de data mining pentru a ob\u021bine perspective bazate pe date. Data mining poate ajuta la segmentarea clien\u021bilor, la identificarea fraudelor, la prognozarea v\u00e2nz\u0103rilor \u0219i multe altele.<\/p>\n<ul>\n<li><b>Segmentarea clien\u021bilor<\/b><br \/>\nPrin analiza datelor despre clien\u021bi \u0219i identificarea tr\u0103s\u0103turilor clien\u021bilor \u021binti\u021bi, companiile pot s\u0103-i grupeze \u00eentr-un segment separat \u0219i s\u0103 le ofere oferte speciale adaptate nevoilor lor.<\/li>\n<li><b>Analiza co\u0219ului de pia\u021b\u0103<\/b><br \/>\nAceast\u0103 tehnic\u0103 se bazeaz\u0103 pe teoria c\u0103 dac\u0103 achizi\u021bionezi un anumit grup de produse, este foarte probabil s\u0103 cumperi \u0219i un alt grup de produse. Un exemplu cunoscut: atunci c\u00e2nd ta\u021bii cump\u0103r\u0103 scutece pentru bebelu\u0219ii lor, ei tind de obicei s\u0103 cumpere bere \u00eempreun\u0103 cu scutecile.<\/li>\n<li><b>Prognozarea v\u00e2nz\u0103rilor<\/b><br \/>\nAceasta poate p\u0103rea similar\u0103 cu analiza co\u0219ului de pia\u021b\u0103, dar de data aceasta analiza datelor este folosit\u0103 pentru a prognoza c\u00e2nd un client va relua achizi\u021bia unui produs \u00een viitor. De exemplu, un antrenor achizi\u021bioneaz\u0103 o cutie de protein\u0103, care ar trebui s\u0103 dureze 9 luni. Magazinul care vinde aceast\u0103 protein\u0103 pl\u0103nuie\u0219te s\u0103 lanseze un nou produs \u00een 9 luni, astfel \u00eenc\u00e2t antrenorul s\u0103-l cumpere din nou.<\/li>\n<li><b>Detectarea fraudei<\/b><br \/>\nExtragerea de date ajut\u0103 la construirea de modele pentru detectarea fraudei. Prin colectarea de exemple de rapoarte frauduloase \u0219i legitime, companiile pot determina care opera\u021biuni sunt suspecte.<\/li>\n<li><b>Detectarea modelurilor \u00een produc\u021bie<\/b><br \/>\n\u00cen industria de prelucrare, extragerea de date este utilizat\u0103 pentru a ajuta la proiectarea sistemelor prin identificarea rela\u021biilor dintre arhitectura produsului, profilul \u0219i nevoile clien\u021bilor. Extragerea de date poate prezice, de asemenea, termenele de dezvoltare a produsului \u0219i costurile.<\/li>\n<\/ul>\n<p>\u0218i acestea sunt doar c\u00e2teva scenarii de utilizare a extragerii de date.<\/p>\n<h2>Etapele Extragerei de Date<\/h2>\n<p>Extragerea de date este un proces integral de colectare, selec\u021bie, cur\u0103\u021bare, transformare \u0219i extragere a datelor pentru a evalua tiparele \u0219i, \u00een cele din urm\u0103, pentru a extrage valoare.<\/p>\n<p><img decoding=\"async\" alt=\"S\u0103 \u00een\u021belegem care este diferen\u021ba dintre Data Mining \u0219i Data Extraction\" src=\"\/wp-content\/uploads\/2020\/10\/0f601a9f32e2b9517f1a7deb0e353b5a.png\" style=\"display:block;margin: 0 auto;\"><\/p>\n<p>\u00cen general, \u00eentregul proces de extragere a datelor poate fi rezumat \u00een 7 etape:<\/p>\n<ol>\n<li><b>Cur\u0103\u021barea datelor<\/b><br \/>\n\u00cen lumea real\u0103, datele nu sunt \u00eentotdeauna cur\u0103\u021bate \u0219i structurate. Adesea, ele sunt zgomotoase, incomplete \u0219i pot con\u021bine erori. Pentru a asigura c\u0103 rezultatul extragerii de date este precis, este necesar s\u0103 cur\u0103\u021b\u0103m datele ini\u021bial. Unele metode de cur\u0103\u021bare includ completarea valorilor lips\u0103, controlul automat \u0219i manual etc.<\/li>\n<li><b>Integrarea datelor<\/b><br \/>\nAceasta este etapa \u00een care datele din diverse surse sunt extrase, combinate \u0219i integrate. Sursele pot fi baze de date, fi\u0219iere text, foi de calcul, documente, matrice de date multidimensionale, internet \u0219i a\u0219a mai departe.<\/li>\n<li><b>Extrac\u021bia datelor<\/b><br \/>\nDe obicei, nu toate datele integrate sunt necesare \u00een extragerea de date. Selec\u021bia datelor este etapa \u00een care dintr-o baz\u0103 de date mare sunt selectate \u0219i extrase doar datele utile.<\/li>\n<li><b>Transformarea datelor<\/b><br \/>\nDup\u0103 selec\u021bia datelor, acestea sunt transformate \u00een forme adecvate pentru extragere. Acest proces include normalizarea, agregarea, generalizarea etc.<\/li>\n<li><b>Analiza inteligent\u0103 a datelor<\/b><br \/>\nAici \u00eencepe cea mai important\u0103 parte a extragerii de date - utilizarea metodelor intelectuale pentru a c\u0103uta tipare \u00een acestea. Procesul include regresia, clasificarea, prognoza, clusteringul, studiul asocia\u021biilor \u0219i multe altele.<\/li>\n<li><b>Evaluarea modelului<\/b><br \/>\nAceast\u0103 etap\u0103 este conceput\u0103 pentru a identifica tiparele poten\u021bial utile, u\u0219or de \u00een\u021beles, precum \u0219i tiparele care confirm\u0103 ipotezele.<\/li>\n<li><b>Reprezentarea cuno\u0219tin\u021belor<\/b><br \/>\n\u00cen etapa final\u0103, informa\u021biile ob\u021binute sunt prezentate \u00eentr-un mod atractiv, utiliz\u00e2nd metode de reprezentare a cuno\u0219tin\u021belor \u0219i vizualizare.<\/li>\n<\/ol>\n<h2>Dezavantajele Data Mining<\/h2>\n<ul>\n<li><b>Investi\u021bii mari de timp \u0219i munc\u0103<\/b><br \/>\nDat fiind c\u0103 procesul de mining al datelor este de lung\u0103 durat\u0103 \u0219i complex, acesta necesit\u0103 o munc\u0103 considerabil\u0103 din partea unor persoane productive \u0219i calificate. Speciali\u0219tii \u00een analiza datelor pot folosi instrumente puternice de mining al datelor, dar au nevoie de exper\u021bi pentru preg\u0103tirea datelor \u0219i \u00een\u021belegerea rezultatelor. Ca urmare, prelucrarea tuturor informa\u021biilor poate dura ceva timp.<\/li>\n<li><b>Confiden\u021bialitatea \u0219i securitatea datelor<\/b><br \/>\nDeoarece data mining colecteaz\u0103 informa\u021bii despre clien\u021bi prin metode de marketing, poate \u00eenc\u0103lca confiden\u021bialitatea utilizatorilor. \u00cen plus, hackerii pot accesa datele stocate \u00een sistemele de mining ale datelor. Aceasta reprezint\u0103 o amenin\u021bare pentru securitatea datelor clien\u021bilor. Dac\u0103 datele furate sunt utilizate \u00een mod necorespunz\u0103tor, aceasta poate provoca r\u0103u altora.<\/li>\n<\/ul>\n<blockquote><p>Mai sus este o scurt\u0103 introducere \u00een data mining. A\u0219a cum am men\u021bionat anterior, data mining include procesul de colectare \u0219i integrare a datelor, care cuprinde procesul de extragere a datelor (data extraction). \u00cen acest caz, se poate spune cu \u00eencredere c\u0103 data extraction poate fi o parte a procesului \u00eendelungat de data mining.<\/p><\/blockquote>\n<h2>Ce este Data Extraction?<\/h2>\n<p>Cunoscut \u0219i sub denumirea de \u201eextragerea datelor web\u201d \u0219i \u201eweb scraping\u201d, acest proces reprezint\u0103 actul de extragere a datelor din surse de date (de obicei neorganizate sau slab organizate) \u00een locuri centralizate, pentru a le stoca sau pentru o procesare ulterioar\u0103. \u00cen special, sursele de date neorganizate includ pagini web, emailuri, documente, fi\u0219iere PDF, text scanat, rapoarte de mainframe, fi\u0219iere pe band\u0103, anun\u021buri, etc. Stoc\u0103rile centralizate pot fi locale, cloud sau hibride. Este important de re\u021binut c\u0103 extragerea datelor nu include procesarea sau alte analize care pot avea loc ulterior.<\/p>\n<h2>Ce se poate face cu ajutorul Data Extraction?<\/h2>\n<p>\u00cen mare parte, scopurile extragerii datelor sunt \u00eemp\u0103r\u021bite \u00een 3 categorii.<\/p>\n<ul>\n<li><b>Arhivare<\/b><br \/>\nExtrac\u021bia de date poate transforma datele din formate fizice, cum ar fi c\u0103r\u021bi, ziare, facturi, \u00een formate digitale, de exemplu, baze de date pentru stocare sau backup.<\/li>\n<li><b>Schimbarea formatului datelor<\/b><br \/>\nC\u00e2nd dori\u021bi s\u0103 transfera\u021bi datele de pe site-ul dvs. actual pe unul nou, aflat \u00een dezvoltare, pute\u021bi colecta datele de pe propriul site extr\u0103g\u00e2ndu-le.<\/li>\n<li><b>Analiza datelor<\/b><br \/>\nSe realizeaz\u0103 adesea o analiz\u0103 suplimentar\u0103 a datelor extrase pentru a ob\u021bine o imagine de ansamblu asupra acestora. Acest lucru poate p\u0103rea similar cu analiza datelor \u00een cadrul data mining, dar re\u021bine\u021bi c\u0103 analiza datelor este scopul extragerii, nu o parte a acesteia. Mai mult, datele sunt analizate diferit. Un exemplu ar fi proprietarii de magazine online care extrag informa\u021bii despre produse de pe site-uri de comer\u021b electronic, precum Amazon, pentru a monitoriza strategiile concuren\u021bilor \u00een timp real. La fel ca \u00een cazul data mining, extragerea datelor este un proces automatizat, av\u00e2nd numeroase avantaje. \u00cen trecut, oamenii copiase \u0219i lipseau datele manual dintr-un loc \u00een altul, ceea ce necesita mult timp. Extrac\u021bia de date accelereaz\u0103 procesul de colectare \u0219i cre\u0219te semnificativ precizia datelor extrase.<\/li>\n<\/ul>\n<h2>C\u00e2teva exemple de aplicare a Extrac\u021biei de date<\/h2>\n<p>Similar cu data mining, extragerea datelor este utilizat\u0103 pe scar\u0103 larg\u0103 \u00een diverse industrii. Pe l\u00e2ng\u0103 monitorizarea pre\u021burilor \u00een comer\u021bul electronic, extragerea de date poate ajuta \u00een cercet\u0103ri proprii, agregarea \u0219tirilor, marketing, \u00een domeniul imobiliar, c\u0103l\u0103torii \u0219i turism, consultan\u021b\u0103, finance \u0219i multe altele.<\/p>\n<ul>\n<li><b>Generarea de lead-uri<\/b><br \/>\nCompaniile pot extrage date din directoare: Yelp, Crunchbase, Yellowpages \u0219i pot genera lead-uri pentru dezvoltarea afacerii. Pute\u021bi viziona videoclipul de mai jos pentru a \u00eenv\u0103\u021ba cum s\u0103 extrage\u021bi date din Yellowpages cu ajutorul <noindex><a rel=\"nofollow\" href=\"https:\/\/www.octoparse.com\/blog\/big-announcement-web-scraping-template-take-away\">template-ului de web scraping<\/a><\/noindex>.<\/p>\n<p><center><iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\/\/embedd.srv.habr.com\/iframe\/5f9016d7d4323713fd415857\" frameborder=\"0\" allowfullscreen=\"\"><\/iframe><\/center><\/li>\n<li><b>Agregarea de con\u021binut \u0219i \u0219tiri<\/b><br \/>\nSite-urile web care agreg\u0103 con\u021binut pot ob\u021bine fluxuri regulate de date din mai multe surse \u0219i \u00ee\u0219i pot men\u021bine site-urile actualizate.<\/li>\n<li><b>Analiza sentimentelor<\/b><br \/>\nDup\u0103 extragerea recenziilor, comentariilor \u0219i testimonialelor din re\u021belele sociale, cum ar fi Instagram \u0219i Twitter, speciali\u0219tii pot analiza opiniile subiacente \u0219i pot ob\u021bine perspective despre cum este perceput brandul, produsul sau un anumit fenomen.<\/li>\n<\/ul>\n<h2>Pa\u0219ii Extrac\u021biei de date<\/h2>\n<p>Extrac\u021bia datelor este prima etap\u0103 a ETL (acronim pentru Extract, Transform, Load: extragere, transformare, \u00eenc\u0103rcare) \u0219i ELT (extragere, \u00eenc\u0103rcare \u0219i transformare). ETL \u0219i ELT sunt, de asemenea, parte integrant\u0103 a unei strategii complete de integrare a datelor. Cu alte cuvinte, extrac\u021bia datelor poate fi o parte a procesului lor de ob\u021binere.<\/p>\n<p><img decoding=\"async\" alt=\"S\u0103 \u00een\u021belegem care este diferen\u021ba dintre Data Mining \u0219i Data Extraction\" src=\"\/wp-content\/uploads\/2020\/10\/25128c2d46b3bc383606efde5862b480.png\" style=\"display:block;margin: 0 auto;\"><br \/>\nExtragere, transformare, \u00eenc\u0103rcare<\/p>\n<p>\u00cen timp ce data mining reprezint\u0103 ob\u021binerea de informa\u021bii din mari volume de date, data extraction este un proces mult mai scurt \u0219i simplu. Acesta poate fi redus la trei etape:<\/p>\n<ol>\n<li><b>Alegerea sursei de date<\/b><br \/>\nSelecta\u021bi sursa din care dori\u021bi s\u0103 extrage\u021bi datele, de exemplu, un site web.<\/li>\n<li><b>Colectarea datelor<\/b><br \/>\nTrimite\u021bi o cerere \u201eGET\u201d c\u0103tre site \u0219i analiza\u021bi documentul HTML ob\u021binut folosind limbaje de programare precum Python, PHP, R, Ruby etc.<\/li>\n<li><b>Stocarea datelor<\/b><br \/>\nSalva\u021bi datele \u00een baza de date local\u0103 sau \u00een cloud pentru utilizare ulterioar\u0103. Dac\u0103 sunte\u021bi un programator experimentat care dore\u0219te s\u0103 extrag\u0103 date, pa\u0219ii men\u021biona\u021bi anterior v\u0103 pot p\u0103rea simpli. Totu\u0219i, dac\u0103 nu programati, exist\u0103 o scurt\u0103tur\u0103 \u2014 utiliza\u021bi instrumente de extrac\u021bie a datelor, cum ar fi <noindex><a rel=\"nofollow\" href=\"http:\/\/www.octoparse.com\/\">Octoparse<\/a><\/noindex>. Instrumentele de data extraction, la fel ca \u0219i cele de data mining, sunt concepute pentru a economisi energie \u0219i a simplifica procesarea datelor pentru to\u021bi. Aceste instrumente nu sunt doar economice, ci \u0219i prietenoase pentru \u00eencep\u0103tori. Ele permit utilizatorilor s\u0103 colecteze date \u00een c\u00e2teva minute, s\u0103 le stocheze \u00een cloud \u0219i s\u0103 le exporte \u00een diferite formate: Excel, CSV, HTML, JSON sau \u00een baze de date pe site prin API.<\/li>\n<\/ol>\n<h2>Dezavantajele Data Extraction<\/h2>\n<ul>\n<li><b>E\u0219ecul serverului<\/b><br \/>\nAtunci c\u00e2nd se extrag date pe scar\u0103 larg\u0103, serverul web al site-ului \u021bint\u0103 poate fi supra\u00eenc\u0103rcat, ceea ce poate duce la defectarea serverului. Acest lucru va d\u0103una intereselor de\u021binatorului site-ului.<\/li>\n<li><b>Blocat pe IP<\/b><br \/>\nC\u00e2nd o persoan\u0103 colecteaz\u0103 date prea des, site-urile web pot bloca adresa sa IP. Resursa poate interzice complet adresa IP sau limita accesul, l\u0103s\u00e2nd datele incomplecte. Pentru a extrage datele \u0219i a evita blocarea, trebuie s\u0103 face\u021bi acest lucru cu o vitez\u0103 moderat\u0103 \u0219i s\u0103 aplica\u021bi unele metode anti-blocare.<\/li>\n<li><b>Probleme legale<\/b><br \/>\nExtrac\u021bia de date de pe web intr\u0103 \u00een zona gri c\u00e2nd vine vorba de legalitate. Site-uri mari, cum ar fi Linkedin \u0219i Facebook, precizeaz\u0103 clar \u00een termenii lor de utilizare c\u0103 orice extragere automat\u0103 de date este interzis\u0103. \u00centre companii au existat multe procese legale din cauza activit\u0103\u021bii boturilor.<\/li>\n<\/ul>\n<h2>Diferen\u021bele cheie \u00eentre Data Mining \u0219i Data Extraction<\/h2>\n<ol>\n<li>Data mining este de asemenea cunoscut sub numele de descoperirea cuno\u0219tin\u021belor \u00een baze de date, extrac\u021bia cuno\u0219tin\u021belor, analiza datelor\/\u0219abloanelor, colectarea informa\u021biilor. Data extraction este utilizat interschimbabil cu extragerea de date de pe web, scanarea paginilor web, colectarea datelor \u0219i a\u0219a mai departe.<\/li>\n<li>Cercet\u0103rile \u00een data mining se bazeaz\u0103 \u00een principal pe date structurate, \u00een timp ce \u00een extragerea de date, acestea sunt, de obicei, extrase din surse nestructurate sau slab structurate.<\/li>\n<li>Scopul data mining-ului este de a face datele mai utile pentru analiz\u0103. Data extraction reprezint\u0103 colectarea datelor \u00eentr-un singur loc unde pot fi stocate sau procesate.<\/li>\n<li>Analiza \u00een data mining se bazeaz\u0103 pe metode matematice pentru identificarea modelelor sau tendin\u021belor. Data extraction se bazeaz\u0103 pe limbaje de programare sau instrumente de extrac\u021bie a datelor pentru a parcurge sursele.<\/li>\n<li>Scopul data mining-ului este de a g\u0103si fapte care anterior nu erau cunoscute sau au fost ignorate, \u00een timp ce data extraction se ocup\u0103 de informa\u021bii existente.<\/li>\n<li>Data mining este mai complicat \u0219i necesit\u0103 investi\u021bii mai mari \u00een instruirea oamenilor. Data extraction, utiliz\u00e2nd instrumentul potrivit, poate fi extrem de simplu \u0219i rentabil.<\/li>\n<\/ol>\n<p>Ajut\u0103m \u00eencep\u0103torii s\u0103 nu se piard\u0103 \u00een Date. Special pentru utilizatorii de pe Habr, am creat un cod promo\u021bional <b>HABR<\/b>, oferind o reducere suplimentar\u0103 de 10% fa\u021b\u0103 de discountul indicat pe banner.<\/p>\n<p><noindex><a rel=\"nofollow\" href=\"https:\/\/skillfactory.ru\/?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_banner&amp;utm_term=regular&amp;utm_content=habr_banner\"><img decoding=\"async\" alt=\"S\u0103 \u00een\u021belegem care este diferen\u021ba dintre Data Mining \u0219i Data Extraction\" src=\"\/wp-content\/uploads\/2020\/10\/19f2c89f80e09bcb31702236b2cbc4d2.png\" style=\"display:block;margin: 0 auto;\"><\/a><\/noindex><\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/skillfactory.ru\/dstpro?utm_source=infopartners&amp;utm_medium=habr&amp;utm_campaign=habr_DSPR&amp;utm_term=regular&amp;utm_content=211020\">\u00cenv\u0103\u021barea profesiei Data Science de la zero<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" 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Data Mining \u0447\u0430\u0441\u0442\u043e \u043d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u043f\u043e\u043d\u0438\u043c\u0430\u044e\u0442 \u043a\u0430\u043a \u0438\u0437\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u0435 \u0438 \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445, \u043d\u043e \u043d\u0430 \u0441\u0430\u043c\u043e\u043c \u0434\u0435\u043b\u0435 \u0432\u0441\u0435 \u043d\u0430\u043c\u043d\u043e\u0433\u043e \u0441\u043b\u043e\u0436\u043d\u0435\u0435. \u0412 \u044d\u0442\u043e\u043c \u043f\u043e\u0441\u0442\u0435 \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u0440\u0430\u0441\u0441\u0442\u0430\u0432\u0438\u043c \u0442\u043e\u0447\u043a\u0438 \u043d\u0430\u0434 Mining \u0438 \u0432\u044b\u044f\u0441\u043d\u0438\u043c \u0440\u0430\u0437\u043d\u0438\u0446\u0443 \u043c\u0435\u0436\u0434\u0443 Data Mining \u0438 Data Extraction. \u0427\u0442\u043e \u0442\u0430\u043a\u043e\u0435 Data Mining? Data mining, \u0442\u0430\u043a\u0436\u0435 \u043d\u0430\u0437\u044b\u0432\u0430\u0435\u043c\u044b\u0439 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":97891,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-97890","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2.1 - aioseo.com -->\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/prohoster.info\/ro\/blog\/administrirovanie\/razbiraemsya-v-chem-raznicza-mezhdu-data-mining-i-data-extraction\" \/>\n\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.2.1\" \/>\n\t\t<meta property=\"og:locale\" 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