{"id":475920,"date":"2023-08-09T07:24:43","date_gmt":"2023-08-09T07:24:43","guid":{"rendered":""},"modified":"2023-09-05T11:11:34","modified_gmt":"2023-09-05T11:11:34","slug":"association-rule-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/association-rule-learning\/","title":{"rendered":"Birliktelik kural\u0131 \u00f6\u011frenme"},"content":{"rendered":"<p>Birliktelik kural\u0131 \u00f6\u011frenme, b\u00fcy\u00fck veri k\u00fcmelerindeki bir dizi \u00f6\u011fe aras\u0131ndaki ilgin\u00e7 ili\u015fkileri veya &#039;ili\u015fkilendirmeleri&#039; ke\u015ffetmek i\u00e7in veri madencili\u011finden yararlanan bir makine \u00f6\u011frenme tekni\u011fidir. Bu bilgiye dayal\u0131 yakla\u015f\u0131m, pazar sepeti analizi, web kullan\u0131m madencili\u011fi, izinsiz giri\u015f tespiti ve s\u00fcrekli \u00fcretim gibi veriye dayal\u0131 \u00e7e\u015fitli alanlarda temel bir ara\u00e7t\u0131r.<\/p>\n<h2>Ge\u00e7mi\u015fe Yolculuk: Birliktelik Kural\u0131 \u00d6\u011freniminin Ba\u015flang\u0131c\u0131<\/h2>\n<p>Bir veri madencili\u011fi tekni\u011fi olarak birliktelik kural\u0131 \u00f6\u011frenimi, \u00f6ncelikle perakende sekt\u00f6r\u00fcnde ba\u015far\u0131l\u0131 bir \u015fekilde uygulanmas\u0131 nedeniyle 1990&#039;lar\u0131n ortas\u0131nda tan\u0131nmaya ba\u015flad\u0131. Birliktelik kurallar\u0131 olu\u015fturmaya y\u00f6nelik ilk \u00f6ne \u00e7\u0131kan algoritma, 1994 y\u0131l\u0131nda Rakesh Agrawal ve Ramakrishnan Srikant taraf\u0131ndan sunulan &#039;Apriori Algoritmas\u0131&#039;yd\u0131. \u00c7al\u0131\u015fma, \u00e7ok miktarda sat\u0131\u015f verisini analiz ederek sat\u0131n alma modellerini tan\u0131ma giri\u015fiminden ortaya \u00e7\u0131kt\u0131.<\/p>\n<h2>Birliktelik Kural\u0131 \u00d6\u011freniminin Derinlemesine \u0130ncelemesi<\/h2>\n<p>Birliktelik kural\u0131 \u00f6\u011frenme, b\u00fcy\u00fck veri k\u00fcmelerindeki bir dizi \u00f6\u011fe aras\u0131nda ilgi \u00e7ekici ili\u015fkiler veya korelasyonlar bulmay\u0131 ama\u00e7layan kural tabanl\u0131 bir makine \u00f6\u011frenme tekni\u011fidir. Ke\u015ffedilen kurallar genellikle &quot;e\u011fer-o halde&quot; ifadeleri olarak ifade edilir. \u00d6rne\u011fin, e\u011fer bir m\u00fc\u015fteri ekmek ve tereya\u011f\u0131 (\u00f6nceki) sat\u0131n al\u0131rsa, o zaman muhtemelen s\u00fct (sonu\u00e7) sat\u0131n alacakt\u0131r. Burada \u201cekmek ve tereya\u011f\u0131\u201d ve \u201cs\u00fct\u201d \u00f6\u011fe k\u00fcmeleridir.<\/p>\n<p>Birliktelik kural\u0131 \u00f6\u011freniminde kural de\u011ferlendirmesinin iki temel \u00f6l\u00e7\u00fcs\u00fc &#039;destek&#039; ve &#039;g\u00fcven&#039;dir. &#039;Destek&#039; bir \u00f6\u011fe k\u00fcmesinin ortaya \u00e7\u0131kma s\u0131kl\u0131\u011f\u0131n\u0131 \u00f6l\u00e7erken, &#039;g\u00fcven&#039; \u00f6\u011felerin \u00f6nc\u00fcl g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda sonu\u00e7ta ortaya \u00e7\u0131kma olas\u0131l\u0131\u011f\u0131n\u0131 yans\u0131t\u0131r. Bir di\u011fer \u00f6l\u00e7\u00fc olan &#039;lift&#039; ise emsal sat\u0131ld\u0131\u011f\u0131nda sonu\u00e7 sat\u0131\u015f oran\u0131ndaki art\u0131\u015f hakk\u0131nda bilgi verebilmektedir.<\/p>\n<h2>Birliktelik Kural\u0131 \u00d6\u011frenmenin Anatomisi<\/h2>\n<p>Birliktelik kural\u0131n\u0131n \u00f6\u011frenilmesi \u00fc\u00e7 ana ad\u0131mdan olu\u015fur:<\/p>\n<ol>\n<li>\u00d6\u011fe k\u00fcmesi olu\u015fturma: S\u0131kl\u0131kla bir arada meydana gelen \u00f6\u011fe veya olay k\u00fcmelerinin tan\u0131mlanmas\u0131.<\/li>\n<li>Kural olu\u015fturma: Bu \u00f6\u011fe k\u00fcmelerinden birliktelik kurallar\u0131 olu\u015fturma.<\/li>\n<li>Kural budama: Destek, g\u00fcven ve y\u00fckselme gibi \u00f6l\u00e7\u00fcmlere dayal\u0131 olarak yararl\u0131 olma olas\u0131l\u0131\u011f\u0131 d\u00fc\u015f\u00fck olan kurallar\u0131n ortadan kald\u0131r\u0131lmas\u0131.<\/li>\n<\/ol>\n<p>S\u0131k g\u00f6r\u00fclen bir \u00f6\u011fe k\u00fcmesinin bir alt k\u00fcmesinin de s\u0131k olmas\u0131 gerekti\u011fini \u00f6ne s\u00fcren Apriori ilkesi, birliktelik kural\u0131 \u00f6\u011freniminin temelini olu\u015fturur. Bu prensip, olas\u0131 olmayan ili\u015fkileri budayarak hesaplama maliyetlerini azaltmada \u00e7ok \u00f6nemlidir.<\/p>\n<h2>Birliktelik Kural\u0131 \u00d6\u011frenmenin Temel \u00d6zellikleri<\/h2>\n<p>Birliktelik kural\u0131 \u00f6\u011freniminin baz\u0131 tan\u0131mlay\u0131c\u0131 \u00f6zellikleri \u015funlard\u0131r:<\/p>\n<ul>\n<li>Denetimsizdir: \u00d6n bilgiye veya etiketli verilere gerek yoktur.<\/li>\n<li>\u00d6l\u00e7eklenebilirlik: B\u00fcy\u00fck veri k\u00fcmelerini i\u015fleyebilir.<\/li>\n<li>Esneklik: Farkl\u0131 alan ve sekt\u00f6rlere uygulanabilir.<\/li>\n<li>Gizli kal\u0131plar\u0131n ke\u015ffi: Hemen g\u00f6r\u00fcnmeyebilecek ili\u015fkileri ve korelasyonlar\u0131 ortaya \u00e7\u0131karabilir.<\/li>\n<\/ul>\n<h2>Birliktelik Kural\u0131 \u00d6\u011frenme T\u00fcrleri<\/h2>\n<p>Birliktelik kural\u0131 \u00f6\u011frenme algoritmalar\u0131 genel olarak iki t\u00fcre ayr\u0131labilir:<\/p>\n<ol>\n<li><strong>Tek boyutlu birliktelik kural\u0131 \u00f6\u011frenme<\/strong>: Bu t\u00fcrde birliktelik kural\u0131n\u0131n \u00f6nc\u00fcl\u00fc ve sonucu \u00f6\u011fe k\u00fcmeleridir. Pazar sepeti analizinde yayg\u0131n olarak kullan\u0131l\u0131r.<\/li>\n<li><strong>\u00c7ok boyutlu birliktelik kural\u0131 \u00f6\u011frenme<\/strong>: Burada kurallar, verinin \u00e7e\u015fitli boyutlar\u0131na veya niteliklerine dayal\u0131 ko\u015fullar i\u00e7erebilir. Bu t\u00fcr genellikle ili\u015fkisel veritabanlar\u0131nda kullan\u0131l\u0131r.<\/li>\n<\/ol>\n<p>Yayg\u0131n olarak kullan\u0131lan birka\u00e7 birliktelik kural\u0131 \u00f6\u011frenme algoritmas\u0131 \u015funlard\u0131r:<\/p>\n<table>\n<thead>\n<tr>\n<th>Algoritma<\/th>\n<th>Tan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u00d6nsel<\/td>\n<td>Aday \u00f6\u011fe k\u00fcmelerini hesaplamak i\u00e7in geni\u015flik \u00f6ncelikli arama stratejisini kullan\u0131r.<\/td>\n<\/tr>\n<tr>\n<td>FP-B\u00fcy\u00fcme<\/td>\n<td>Veritaban\u0131n\u0131 FP a\u011fac\u0131 olarak bilinen yo\u011funla\u015ft\u0131r\u0131lm\u0131\u015f, daha kompakt bir yap\u0131ya s\u0131k\u0131\u015ft\u0131rmak i\u00e7in b\u00f6l ve y\u00f6net yakla\u015f\u0131m\u0131n\u0131 kullan\u0131r.<\/td>\n<\/tr>\n<tr>\n<td>\u00dcST\u00dcN BA\u015eARI<\/td>\n<td>Apriori algoritmas\u0131n\u0131n geleneksel geni\u015flik \u00f6ncelikli yakla\u015f\u0131m\u0131 yerine derinlik \u00f6ncelikli arama stratejisini kullan\u0131r.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Birliktelik Kural\u0131 \u00d6\u011freniminden Yararlanma: Kullan\u0131m, Zorluklar ve \u00c7\u00f6z\u00fcmler<\/h2>\n<p>Birliktelik kural\u0131 \u00f6\u011frenimi a\u015fa\u011f\u0131dakiler de dahil olmak \u00fczere \u00e7e\u015fitli alanlarda uygulama alan\u0131 bulur:<\/p>\n<ul>\n<li><strong>Pazarlama<\/strong>: \u00dcr\u00fcn ili\u015fkilerini belirlemek ve pazarlama stratejilerini geli\u015ftirmek.<\/li>\n<li><strong>Web Kullan\u0131m\u0131 Madencili\u011fi<\/strong>: Kullan\u0131c\u0131 davran\u0131\u015f\u0131n\u0131 belirlemek ve web sitesi d\u00fczenini iyile\u015ftirmek.<\/li>\n<li><strong>T\u0131bbi te\u015fhis<\/strong>: Hasta \u00f6zellikleri ile hastal\u0131klar aras\u0131ndaki ili\u015fkilerin bulunmas\u0131.<\/li>\n<\/ul>\n<p>Birliktelik kural\u0131 \u00f6\u011frenimi \u00f6nemli faydalar sunsa da a\u015fa\u011f\u0131daki gibi sorunlarla kar\u015f\u0131la\u015fabilir:<\/p>\n<ul>\n<li><strong>\u00c7ok say\u0131da olu\u015fturulan kural<\/strong>: B\u00fcy\u00fck veritabanlar\u0131 i\u00e7in \u00e7ok fazla say\u0131da kural olu\u015fturulabilir. Bu durum, destek ve g\u00fcven e\u015fiklerinin art\u0131r\u0131lmas\u0131 veya kural olu\u015fturma s\u0131ras\u0131nda k\u0131s\u0131tlamalar\u0131n kullan\u0131lmas\u0131yla hafifletilebilir.<\/li>\n<li><strong>Kurallar\u0131n yorumlanmas\u0131nda zorluk<\/strong>: Olu\u015fturulan kurallar bir ili\u015fkiyi g\u00f6sterebilir ancak mutlaka nedensellik anlam\u0131na gelmez. Dikkatli bir yorum gereklidir.<\/li>\n<\/ul>\n<h2>Benzer Tekniklerle Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<p>Birliktelik kural\u0131 \u00f6\u011frenimi, di\u011fer makine \u00f6\u011frenimi ve veri madencili\u011fi teknikleriyle baz\u0131 benzerlikler payla\u015fsa da, belirgin farkl\u0131l\u0131klar vard\u0131r:<\/p>\n<table>\n<thead>\n<tr>\n<th>Teknik<\/th>\n<th>Tan\u0131m<\/th>\n<th>benzerlikler<\/th>\n<th>Farkl\u0131l\u0131klar<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Birliktelik Kural\u0131 \u00d6\u011frenimi<\/strong><\/td>\n<td>Bir dizi \u00f6\u011fe aras\u0131nda s\u0131k g\u00f6r\u00fclen kal\u0131plar\u0131, ili\u015fkileri veya korelasyonlar\u0131 bulur<\/td>\n<td>B\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015fabilir; denetimsiz<\/td>\n<td>Bir hedef de\u011fer tahmin etmiyor<\/td>\n<\/tr>\n<tr>\n<td><strong>s\u0131n\u0131fland\u0131rma<\/strong><\/td>\n<td>Kategorik etiketleri tahmin eder<\/td>\n<td>B\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015fabilir<\/td>\n<td>Denetlenen; bir hedef de\u011fer tahmin eder<\/td>\n<\/tr>\n<tr>\n<td><strong>K\u00fcmeleme<\/strong><\/td>\n<td>Benzer \u00f6rnekleri \u00f6zelliklerine g\u00f6re grupland\u0131r\u0131r<\/td>\n<td>Denetimsiz; b\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015fabilir<\/td>\n<td>Kurallar\u0131 tan\u0131mlamaz; yaln\u0131zca verileri k\u00fcmeler<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Birliktelik Kural\u0131 \u00d6\u011freniminin Gelece\u011fi<\/h2>\n<p>Verilerin hacmi ve karma\u015f\u0131kl\u0131\u011f\u0131 artmaya devam ettik\u00e7e birliktelik kural\u0131 \u00f6\u011freniminin gelece\u011fi umut verici g\u00f6r\u00fcn\u00fcyor. Da\u011f\u0131t\u0131lm\u0131\u015f hesaplama ve paralel i\u015flemedeki geli\u015fmeler, daha b\u00fcy\u00fck veri k\u00fcmelerinde birliktelik kural\u0131n\u0131n \u00f6\u011frenilmesine y\u00f6nelik i\u015flem s\u00fcresini h\u0131zland\u0131rabilir. Ayr\u0131ca yapay zeka ve makine \u00f6\u011frenimindeki ilerlemeler, karma\u015f\u0131k veri yap\u0131lar\u0131n\u0131 ve t\u00fcrlerini i\u015fleyebilen daha karma\u015f\u0131k ve incelikli birliktelik kural\u0131 \u00f6\u011frenme algoritmalar\u0131na yol a\u00e7abilir.<\/p>\n<h2>Birliktelik Kural\u0131 \u00d6\u011frenme ve Proxy Sunucular\u0131<\/h2>\n<p>Proxy sunucular\u0131, farkl\u0131 web sitelerindeki kullan\u0131c\u0131 davran\u0131\u015f\u0131 verilerini toplamak ve bir araya getirmek i\u00e7in kullan\u0131labilir. Bu veriler, kullan\u0131c\u0131 davran\u0131\u015f kal\u0131plar\u0131n\u0131 anlamak, hizmeti iyile\u015ftirmek ve g\u00fcvenli\u011fi art\u0131rmak i\u00e7in birliktelik kural\u0131 \u00f6\u011frenimi kullan\u0131larak i\u015flenebilir. Ayr\u0131ca, proxy&#039;ler veri toplamay\u0131 anonimle\u015ftirerek gizlili\u011fi ve etik uyumu garanti edebilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<p>Birliktelik Kural\u0131 \u00d6\u011frenimi hakk\u0131nda daha fazla bilgi edinmek isteyenler i\u00e7in baz\u0131 yararl\u0131 kaynaklar \u015funlard\u0131r:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.kdnuggets.com\/2020\/01\/association-rule-mining.html\" target=\"_new\" rel=\"noopener nofollow\">Birliktelik Kural\u0131 Madencili\u011fine Giri\u015f<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/understanding-association-rule-mining-with-examples-1f907e8157a1\" target=\"_new\" rel=\"noopener nofollow\">\u00d6rneklerle Birliktelik Kural\u0131 \u00d6\u011frenimini Anlamak<\/a><\/li>\n<li><a href=\"https:\/\/www.geeksforgeeks.org\/frequent-pattern-growth-algorithm-in-data-mining\/\" target=\"_new\" rel=\"noopener nofollow\">Veri Madencili\u011finde S\u0131k Desen (FP) B\u00fcy\u00fcme Algoritmas\u0131<\/a><\/li>\n<li><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10462-018-9646-1\" target=\"_new\" rel=\"noopener nofollow\">Birliktelik kural\u0131 madencili\u011fi \u00fczerine bir ara\u015ft\u0131rma<\/a><\/li>\n<\/ul>","protected":false},"featured_media":467648,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475920","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Association Rule Learning: Unleashing the Power of Data Mining<\/mark>","faq_items":[{"question":"What is Association Rule Learning?","answer":"<p>Association Rule Learning is a machine learning method that discovers interesting relationships, or 'associations', among a set of items in large datasets. This technique is widely used in various data-driven domains such as market basket analysis, web usage mining, intrusion detection, and continuous production.<\/p>"},{"question":"When was Association Rule Learning first introduced?","answer":"<p>Association Rule Learning was first recognized in the mid-1990s, with the creation of the 'Apriori Algorithm' by Rakesh Agrawal and Ramakrishnan Srikant in 1994. This algorithm was initially developed to find purchasing patterns by analyzing large amounts of sales data.<\/p>"},{"question":"How does Association Rule Learning work?","answer":"<p>Association Rule Learning works in three primary steps: generating itemsets, creating association rules from these itemsets, and pruning unlikely rules based on measures like support, confidence, and lift. The rules discovered are often expressed as \"if-then\" statements.<\/p>"},{"question":"What are the key features of Association Rule Learning?","answer":"<p>Key features of Association Rule Learning include its unsupervised nature, scalability, flexibility, and its ability to discover hidden patterns in large datasets.<\/p>"},{"question":"What are the types of Association Rule Learning?","answer":"<p>Association Rule Learning algorithms can be broadly classified into two types: Single-dimensional association rule learning and Multidimensional association rule learning. Single-dimensional association rule learning is commonly used in market basket analysis, while Multidimensional association rule learning is often employed in relational databases.<\/p>"},{"question":"How is Association Rule Learning used?","answer":"<p>Association Rule Learning is used in various areas such as marketing to identify product associations, in web usage mining to identify user behavior, and in medical diagnosis to find associations between patient characteristics and diseases.<\/p>"},{"question":"What are the future perspectives related to Association Rule Learning?","answer":"<p>As data continues to grow in volume and complexity, the future of Association Rule Learning looks promising. Advances in distributed computing and parallel processing, as well as developments in artificial intelligence and machine learning, can lead to more sophisticated and nuanced Association Rule Learning algorithms.<\/p>"},{"question":"How can proxy servers be associated with Association Rule Learning?","answer":"<p>Proxy servers can gather and aggregate user behavior data across different websites. This data can be processed using Association Rule Learning to understand user behavior patterns, improve service, and enhance security. Furthermore, proxies can anonymize data collection, ensuring privacy and ethical compliance.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/475920","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/475920\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/467648"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=475920"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}