{"id":13212,"date":"2023-08-25T00:00:00","date_gmt":"2023-08-25T00:00:00","guid":{"rendered":"https:\/\/tech-lib.eu\/tech\/jak-funguje-k-means-komplexni-pruvodce-shlukovanim-k-means\/"},"modified":"2023-08-25T00:00:00","modified_gmt":"2023-08-25T00:00:00","slug":"jak-funguje-k-means-komplexni-pruvodce-shlukovanim-k-means","status":"publish","type":"post","link":"https:\/\/tech-lib.eu\/tech\/jak-funguje-k-means-komplexni-pruvodce-shlukovanim-k-means\/","title":{"rendered":"Jak funguje K-Means. Komplexn\u00ed pr\u016fvodce shlukov\u00e1n\u00edm K-Means"},"content":{"rendered":"
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Na czym polega metoda k \u015brednich?<\/div>\n
Ta procedura umo\u017cliwia podj\u0119cie pr\u00f3by identyfikacji wzgl\u0119dnie jednorodnych grup obserwacji w oparciu o wybran\u0105 charakterystyk\u0119 i z wykorzystaniem algorytmu umo\u017cliwiaj\u0105cego obs\u0142ug\u0119 du\u017cej liczby obserwacji. Zastosowanie algorytmu wymaga jednak od u\u017cytkownika okre\u015blenia liczby skupie\u0144.<\/div>\n
\n\t\t\t\t\t Dal\u0161\u00ed informace najdete na<\/span> www.ibm.com<\/a>\n\t\t\t\t<\/div>\n<\/p><\/div>\n
P\u0159edstavte se: Obl\u00edben\u00fdm nekontrolovan\u00fdm p\u0159\u00edstupem strojov\u00e9ho u\u010den\u00ed pro seskupov\u00e1n\u00ed datov\u00fdch bod\u016f na z\u00e1klad\u011b podobnost\u00ed je shlukov\u00e1n\u00ed K-means. Dolov\u00e1n\u00ed dat, rozpozn\u00e1v\u00e1n\u00ed vzor\u016f, segmentace obr\u00e1zk\u016f a segmentace z\u00e1kazn\u00edk\u016f jsou jen n\u011bkter\u00e9 z oblast\u00ed, kde se b\u011b\u017en\u011b pou\u017e\u00edv\u00e1. V tomto \u010dl\u00e1nku se budeme zab\u00fdvat specifiky fungov\u00e1n\u00ed K-means, jeho f\u00e1zemi a faktory, kter\u00e9 p\u0159isp\u00edvaj\u00ed k jeho \u0161irok\u00e9mu vyu\u017eit\u00ed. Co je p\u0159\u00edstup K-Means? <\/p>\n
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Soubor dat je rozd\u011blen do K shluk\u016f pomoc\u00ed shlukovac\u00edho algoritmu K-means zalo\u017een\u00e9ho na rozd\u011blen\u00ed. U\u017eivatelem stanoven\u00e1 hodnota K ozna\u010duje po\u017eadovan\u00fd po\u010det shluk\u016f. Ka\u017ed\u00e9mu datov\u00e9mu bodu je p\u0159i\u0159azen shluk, jeho\u017e centroid (st\u0159edn\u00ed hodnota) je mu podle algoritmu nejbl\u00ed\u017ee. P\u0159i neust\u00e1l\u00e9m zp\u0159es\u0148ov\u00e1n\u00ed shluk\u016f a\u017e do konvergence minimalizuje sou\u010det \u010dtvercov\u00fdch vzd\u00e1lenost\u00ed mezi datov\u00fdmi body a jim p\u0159i\u0159azen\u00fdmi centroidy. Kroky algoritmu K-Means jsou n\u00e1sleduj\u00edc\u00ed: <\/p>\n

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1. Inicializace: N\u00e1hodn\u011b se vybere K datov\u00fdch bod\u016f, kter\u00e9 budou slou\u017eit jako po\u010d\u00e1te\u010dn\u00ed centroidy. 2. P\u0159i\u0159azen\u00ed: Na z\u00e1klad\u011b euklidovsk\u00e9 vzd\u00e1lenosti um\u00edst\u011bte ka\u017ed\u00fd datov\u00fd bod vedle jeho nejbli\u017e\u0161\u00edho centroidu. 3. P\u0159epo\u010d\u00edtejte centroidy zpr\u016fm\u011brov\u00e1n\u00edm v\u0161ech datov\u00fdch bod\u016f p\u0159i\u0159azen\u00fdch ke ka\u017ed\u00e9mu shluku. 4. Opakov\u00e1n\u00ed: Pokra\u010dujte v kroc\u00edch 2 a 3, dokud nedojde ke konvergenci, co\u017e je okam\u017eik, kdy se centroidy p\u0159estanou znateln\u011b m\u011bnit, nebo dokud nen\u00ed dosa\u017eeno p\u0159id\u011blen\u00e9ho po\u010dtu iterac\u00ed. <\/p>\n

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Jak funguje metoda K-Means? N\u00e1hodn\u011b se vybere K datov\u00fdch bod\u016f, kter\u00e9 slou\u017e\u00ed jako po\u010d\u00e1te\u010dn\u00ed centroidy v inicializa\u010dn\u00edm procesu. Tyto body slou\u017e\u00ed jako sem\u00ednka shluk\u016f. Ka\u017ed\u00fd datov\u00fd bod je pak p\u0159i\u0159azen ke shluku, jeho\u017e centroid je mu algoritmem nejbl\u00ed\u017ee. K tomu se vypo\u010d\u00edt\u00e1 euklidovsk\u00e1 vzd\u00e1lenost mezi ka\u017ed\u00fdm datov\u00fdm bodem a ka\u017ed\u00fdm centroidem. Shluk s nejkrat\u0161\u00ed vzd\u00e1lenost\u00ed je p\u0159i\u0159azen datov\u00e9mu bodu. <\/p>\n

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Po dokon\u010den\u00ed p\u016fvodn\u00edho p\u0159i\u0159azen\u00ed algoritmus p\u0159ejde do f\u00e1ze aktualizace. Zde se centroidy p\u0159epo\u010d\u00edtaj\u00ed zpr\u016fm\u011brov\u00e1n\u00edm v\u0161ech datov\u00fdch bod\u016f p\u0159i\u0159azen\u00fdch ke ka\u017ed\u00e9mu shluku. To znamen\u00e1, \u017ee centroid ka\u017ed\u00e9ho shluku je upraven tak, aby odr\u00e1\u017eel pr\u016fm\u011brnou polohu v\u0161ech jeho datov\u00fdch bod\u016f. Tento proces se opakuje, dokud se centroidy nep\u0159estanou v\u00fdrazn\u011b m\u011bnit nebo dokud nen\u00ed dosa\u017eeno p\u0159edem stanoven\u00e9ho po\u010dtu iterac\u00ed. <\/p>\n

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Shlukov\u00e1n\u00ed K-Means: Pro\u010d ho pou\u017e\u00edvat? <\/p>\n

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Shlukov\u00e1n\u00ed K-means se \u010dasto pou\u017e\u00edv\u00e1 v mnoha oblastech z mnoha d\u016fvod\u016f. P\u0159edev\u0161\u00edm m\u00e1 dobr\u00fd v\u00fdpo\u010detn\u00ed v\u00fdkon a dok\u00e1\u017ee si poradit s velk\u00fdmi soubory dat s velk\u00fdm mno\u017estv\u00edm prom\u011bnn\u00fdch. Proto\u017ee je tento p\u0159\u00edstup p\u0159\u00edmo\u010dar\u00fd a lze jej prov\u00e1d\u011bt rychle, mohou jej vyu\u017e\u00edvat aplikace pracuj\u00edc\u00ed v re\u00e1ln\u00e9m \u010dase. Velkou v\u00fdhodou je tak\u00e9 to, jak snadno lze v\u00fdsledky pochopit. K-means shluky jasn\u011b odd\u011bluj\u00ed datov\u00e9 body, co\u017e usnad\u0148uje pochopen\u00ed a vyhodnocen\u00ed z\u00e1kladn\u00edch vzorc\u016f a korelac\u00ed. <\/p>\n

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Dal\u0161\u00ed v\u00fdhodou shlukov\u00e1n\u00ed K-means je, \u017ee se jedn\u00e1 o metodu u\u010den\u00ed bez dohledu, kter\u00e1 nevy\u017eaduje ozna\u010den\u00e1 data. D\u00edky tomu je v\u00fdhodn\u00e1 p\u0159i pr\u00e1ci se soubory dat, kter\u00e9 nejsou ozna\u010den\u00e9, nebo kdy\u017e nen\u00ed jasn\u00e9, jak\u00e9 jsou skute\u010dn\u00e9 zna\u010dky. Metoda K-means dok\u00e1\u017ee naj\u00edt skryt\u00e9 struktury a klasifikovat datov\u00e9 body podle toho, jak podobn\u00e9 jsou si jejich vlastnosti. <\/p>\n

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Shrnut\u00ed shlukov\u00e1n\u00ed K-means je flexibiln\u00ed a \u00fasp\u011b\u0161n\u00fd p\u0159\u00edstup ke shlukov\u00e1n\u00ed datov\u00fdch bod\u016f. K-means efektivn\u011b rozd\u011bluje soubor dat iterativn\u00edm umis\u0165ov\u00e1n\u00edm datov\u00fdch bod\u016f na nejbli\u017e\u0161\u00ed centroidy a aktualizac\u00ed centroid\u016f. Je obl\u00edbenou volbou v mnoha r\u016fzn\u00fdch odv\u011btv\u00edch d\u00edky sv\u00e9 jednoduchosti, interpretovatelnosti a schopnosti zpracov\u00e1vat obrovsk\u00e9 soubory dat. K-means je i nad\u00e1le \u00fa\u010dinn\u00fdm n\u00e1strojem pro hled\u00e1n\u00ed vzor\u016f a vyvozov\u00e1n\u00ed pronikav\u00fdch z\u00e1v\u011br\u016f z dat, a\u0165 u\u017e se pou\u017e\u00edv\u00e1 pro segmentaci z\u00e1kazn\u00edk\u016f, dolov\u00e1n\u00ed dat nebo segmentaci obr\u00e1zk\u016f.<\/p><\/div>\n

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FAQ<\/div>\n
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Co je k-means clustering for dummies?<\/div>\n

Pro za\u010d\u00e1te\u010dn\u00edky: K-means clustering odkazuje na stru\u010dn\u00e9 vysv\u011btlen\u00ed algoritmu k-means clustering, kter\u00fd je \u0161iroce pou\u017e\u00edvanou technikou pro shlukov\u00e1n\u00ed datov\u00fdch bod\u016f. Zjednodu\u0161en\u011b \u0159e\u010deno, shlukov\u00e1n\u00ed K-means se sna\u017e\u00ed rozd\u011blit sadu datov\u00fdch bod\u016f do k r\u016fzn\u00fdch skupin podle toho, jak jsou si podobn\u00e9. <\/p>\n

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Algoritmus n\u00e1hodn\u011b vybere k po\u010d\u00e1te\u010dn\u00edch center shluk\u016f, opakovan\u011b p\u0159i\u0159ad\u00ed ka\u017ed\u00fd datov\u00fd bod k nejbli\u017e\u0161\u00edmu centru shluku a pot\u00e9 aktualizuje centra shluk\u016f podle pr\u016fm\u011bru bod\u016f v ka\u017ed\u00e9m shluku. Tento postup se opakuje, dokud ji\u017e nedojde k \u017e\u00e1dn\u00fdm znateln\u00fdm zm\u011bn\u00e1m v p\u0159i\u0159azen\u00ed shluk\u016f a st\u0159ed\u016f. <\/p>\n

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Minimalizace sou\u010dtu \u010dtvercov\u00fdch vzd\u00e1lenost\u00ed mezi ka\u017ed\u00fdm datov\u00fdm bodem a odpov\u00eddaj\u00edc\u00edm st\u0159edem shluku je c\u00edlem shlukov\u00e1n\u00ed k-means. Vytvo\u0159en\u00e9 shluky lze vyu\u017e\u00edt pro \u0159adu \u00faloh, v\u010detn\u011b rozpozn\u00e1v\u00e1n\u00ed vzor\u016f, segmentace dat a predikce. <\/p>\n

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Celkov\u011b k-means clustering for dummies nab\u00edz\u00ed jasn\u00e9 vysv\u011btlen\u00ed z\u00e1kladn\u00ed my\u0161lenky a krok\u016f t\u00e9to techniky. <\/p>\n<\/div>\n

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Je K-means nejlep\u0161\u00ed shlukovac\u00ed algoritmus?<\/div>\n

T\u00e9ma mohu analyzovat jako jazykov\u00fd model um\u011bl\u00e9 inteligence, ale uv\u011bdomte si, \u017ee v\u00fdb\u011br „nejlep\u0161\u00ed“ shlukovac\u00ed techniky z\u00e1vis\u00ed na \u0159ad\u011b prom\u011bnn\u00fdch, v\u010detn\u011b souboru dat, zam\u00fd\u0161len\u00fdch v\u00fdsledk\u016f a konkr\u00e9tn\u00edch specifikac\u00ed aktu\u00e1ln\u00edho \u00fakolu. <\/p>\n

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Obl\u00edben\u00fdm shlukovac\u00edm algoritmem je K-means, proto\u017ee je p\u0159\u00edmo\u010dar\u00fd, efektivn\u00ed a jednoduch\u00fd na pou\u017eit\u00ed. V z\u00e1vislosti na okolnostech v\u0161ak nen\u00ed v\u017edy tou nejlep\u0161\u00ed volbou. Zde je n\u011bkolik v\u011bc\u00ed, na kter\u00e9 je t\u0159eba myslet: Mezi v\u00fdhody K-means pat\u0159\u00ed: K-means je obl\u00edbenou volbou pro za\u010d\u00e1te\u010dn\u00edky, proto\u017ee je jednoduch\u00fd na pou\u017eit\u00ed a pochopen\u00ed. Je v\u00fdpo\u010detn\u011b efektivn\u00ed, tak\u017ee je vhodn\u00fd pro velk\u00e9 soubory dat. K-means si porad\u00ed s obrovsk\u00fdm po\u010dtem datov\u00fdch bod\u016f a prom\u011bnn\u00fdch, co\u017e se ozna\u010duje jako \u0161k\u00e1lovatelnost. <\/p>\n

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4. Interpretovatelnost: Vytvo\u0159en\u00e9 centroidy shluk\u016f lze pova\u017eovat za reprezentace r\u016fzn\u00fdch skupin dat. 1. P\u0159edpoklady dat: K-means p\u0159edpokl\u00e1d\u00e1, \u017ee shluky jsou kulovit\u00e9, maj\u00ed stejnou velikost a srovnatelnou hustotu, co\u017e nemus\u00ed b\u00fdt v\u017edy pravda. Inicializace, <\/p>\n

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2. Citlivost: V z\u00e1vislosti na tom, kde jsou centroidy na za\u010d\u00e1tku um\u00edst\u011bny, m\u016f\u017ee v\u00fdstup algoritmu poskytovat r\u016fzn\u00e9 v\u00fdsledky. 3. Citlivost na odlehl\u00e9 hodnoty: Proto\u017ee odlehl\u00e9 hodnoty mohou v\u00fdznamn\u011b ovlivnit tvorbu shluk\u016f, je na n\u011b algoritmus K-means citliv\u00fd. 4. Ur\u010den\u00ed optim\u00e1ln\u00edho K: V\u00fdb\u011br spr\u00e1vn\u00e9ho po\u010dtu shluk\u016f (K) m\u016f\u017ee b\u00fdt obt\u00ed\u017en\u00fd a m\u016f\u017ee vy\u017eadovat pou\u017eit\u00ed dal\u0161\u00edch technik nebo znalost\u00ed specifick\u00fdch pro danou oblast. <\/p>\n

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Vzhledem k nev\u00fdhod\u00e1m mohou b\u00fdt v z\u00e1vislosti na konkr\u00e9tn\u00ed problematice vhodn\u011bj\u0161\u00ed jin\u00e9 shlukovac\u00ed algoritmy. Hierarchick\u00e9 shlukov\u00e1n\u00ed, DBSCAN, Gaussovsk\u00e9 sm\u011bsov\u00e9 modely (GMM) a spektr\u00e1ln\u00ed shlukov\u00e1n\u00ed jsou n\u011bkter\u00fdmi n\u00e1hradami K-means. Optim\u00e1ln\u00ed volba z\u00e1vis\u00ed na dostupn\u00fdch datech a c\u00edlech anal\u00fdzy. Ka\u017ed\u00e1 metoda m\u00e1 sv\u00e9 vlastn\u00ed v\u00fdhody a nev\u00fdhody. <\/p>\n

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I kdy\u017e je tedy metoda K-means obl\u00edbenou a \u010dasto pou\u017e\u00edvanou technikou shlukov\u00e1n\u00ed, nemus\u00ed b\u00fdt v\u017edy tou nejlep\u0161\u00ed volbou pro v\u0161echny okolnosti. P\u0159i v\u00fdb\u011bru techniky shlukov\u00e1n\u00ed je nezbytn\u00e9 zohlednit vlastnosti dat a p\u0159esn\u00e9 pot\u0159eby \u00falohy.<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Na czym polega metoda k \u015brednich? Ta procedura umo\u017cliwia podj\u0119cie pr\u00f3by identyfikacji wzgl\u0119dnie jednorodnych grup obserwacji w oparciu o wybran\u0105 charakterystyk\u0119 i z wykorzystaniem algorytmu umo\u017cliwiaj\u0105cego obs\u0142ug\u0119 du\u017cej liczby obserwacji. Zastosowanie algorytmu wymaga jednak od u\u017cytkownika okre\u015blenia liczby skupie\u0144. Dal\u0161\u00ed informace najdete na www.ibm.com P\u0159edstavte se: Obl\u00edben\u00fdm nekontrolovan\u00fdm p\u0159\u00edstupem strojov\u00e9ho u\u010den\u00ed pro seskupov\u00e1n\u00ed datov\u00fdch bod\u016f … \u010c\u00edst d\u00e1l<\/a><\/p>\n","protected":false},"author":4365,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4882],"tags":[],"class_list":["post-13212","post","type-post","status-publish","format-standard","hentry","category-strojove-uceni"],"_links":{"self":[{"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/posts\/13212","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/users\/4365"}],"replies":[{"embeddable":true,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/comments?post=13212"}],"version-history":[{"count":0,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/posts\/13212\/revisions"}],"wp:attachment":[{"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/media?parent=13212"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/categories?post=13212"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/tags?post=13212"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}