<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[RSS Feed]]></title><description><![CDATA[RSS Feed]]></description><link>http://direct.ecency.com</link><image><url>http://direct.ecency.com/logo512.png</url><title>RSS Feed</title><link>http://direct.ecency.com</link></image><generator>RSS for Node</generator><lastBuildDate>Tue, 21 Apr 2026 04:58:26 GMT</lastBuildDate><atom:link href="http://direct.ecency.com/created/clustering/rss.xml" rel="self" type="application/rss+xml"/><item><title><![CDATA[Aphrodite of Cyrene]]></title><description><![CDATA[source Aphrodite of Cyrene She is overwhelming, beautiful, breathlessly so. But, what if this woman were walking down the street? Would vendors give her apples, flowers, bread, jewelry made for the gods,]]></description><link>http://direct.ecency.com/poetry/@tristancarax/aphrodite-of-cyrene</link><guid isPermaLink="true">http://direct.ecency.com/poetry/@tristancarax/aphrodite-of-cyrene</guid><category><![CDATA[poetry]]></category><dc:creator><![CDATA[tristancarax]]></dc:creator><pubDate>Fri, 30 Aug 2019 23:49:00 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc15VtwBcpcNRduYYp5vfHxZnGaBMuEFRUGXECvTXGCTBUxjGmB3vYkVvP3aveRcnbZUdVvHMocHvHSDHmiMHuSBWziX9cqHYv8AeHBoc1roRvrg2Xp?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Effektiv Probleme Lösen durch Clustern/Effective Problem Solving through Clustering]]></title><description><![CDATA[Fehlen Euch auch manchmal die Ideen zum schreiben? Denkt Ihr lang über ein Problem nach und findet die Lösung nicht? Dann versucht einfach einmal zu Clustern! Unser Gehirn ist ein Phantastisches Gebilde]]></description><link>http://direct.ecency.com/deutsch/@patzakandreas/effektiv-probleme-loesen-durch-clustern</link><guid isPermaLink="true">http://direct.ecency.com/deutsch/@patzakandreas/effektiv-probleme-loesen-durch-clustern</guid><category><![CDATA[deutsch]]></category><dc:creator><![CDATA[patzakandreas]]></dc:creator><pubDate>Thu, 07 Feb 2019 21:10:30 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9ULRenmNf6fdGWYSH7NWHfKa4K5o43EnPUXzkHnj6JzcziDRXSLdKp6ivFXPdCHo7uGaift6bHQxHPc?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A: What is clustering in Data Science?]]></title><description><![CDATA[In a data-science context, clustering refers to organizing data into categories by using some sort of distance metric. "K-means clustering" is a common technique for doing so, but other clustering]]></description><link>http://direct.ecency.com/stemq/@markgritter/a-what-is-clustering-in-data-science1545973425852</link><guid isPermaLink="true">http://direct.ecency.com/stemq/@markgritter/a-what-is-clustering-in-data-science1545973425852</guid><category><![CDATA[stemq]]></category><dc:creator><![CDATA[markgritter]]></dc:creator><pubDate>Fri, 28 Dec 2018 05:03:39 GMT</pubDate><enclosure url="https://images.ecency.com/p/C3TZR1g81UNaPs7vzNXHueW5ZM76DSHWEY7onmfLxcK2iP9JddrHUyuxipAzkSrfWEieSP1dv1ThMZfoVzNhazJAEHuAijpnHw59UDPwEBn9B3Nqo8iNvhk?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Unsupervised Machine Learning (KMeans Clustering) with Scikit-Learn]]></title><description><![CDATA[Machine learning can be divided into two main categories, supervised machine learning and unsupervised machine learning. In supervised machine learning, we initially provide the data with it's corresponding]]></description><link>http://direct.ecency.com/machinelearning/@charlesssjb/unsupervised-machine-learning-kmeans-clustering-with-scikit-learn</link><guid isPermaLink="true">http://direct.ecency.com/machinelearning/@charlesssjb/unsupervised-machine-learning-kmeans-clustering-with-scikit-learn</guid><category><![CDATA[machinelearning]]></category><dc:creator><![CDATA[charlesssjb]]></dc:creator><pubDate>Sat, 21 Apr 2018 18:05:30 GMT</pubDate><enclosure url="https://images.ecency.com/p/2gsjgna1uruvUuS7ndh9YqVwYGPLVszbFLwwpAYXZuQfD7TrpfowcyrmTzWyVqeWHdqZDXkkWGCkYqVFyjHCiRTA4G7H27TH7CfhP4wLkcZvZtnLQE?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[R을 이용한 K-means 클러스터링 (Clustering)]]></title><description><![CDATA[개요 Clustering은 Unsupervised learning 기법에 해당한다. 그말은 즉 데이터들의 정답을 알 수 없다는 말인데... 무슨말인가하면 각 개체의 그룹 정보 없이 비슷한 것끼리 묶는다는 말이다. 예를 들어 아래와 같은 여섯 명의 데이터가 있다고 했을 때, col2와 col3의 데이터만으로 비슷한 개체끼리 묶는 것을 군집화-Clustering이라]]></description><link>http://direct.ecency.com/kr/@forensicmon/r-k-means-clustering</link><guid isPermaLink="true">http://direct.ecency.com/kr/@forensicmon/r-k-means-clustering</guid><category><![CDATA[kr]]></category><dc:creator><![CDATA[forensicmon]]></dc:creator><pubDate>Wed, 11 Apr 2018 07:16:30 GMT</pubDate><enclosure url="https://images.ecency.com/p/2gsjgna1uruvUuS7ndh9YqVwYGPLVszbFLwwpAYXZxrsxj2NL4WRb9QrCv3WfQVDNYnnQJGiHpTeqyxwX8Z45bBPyXiG92AdAo9RcVsLcEJDFSfdjL?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Introduction to Clustering with IoT vibration (accelerometer) sensor data]]></title><description><![CDATA[www.coursera.org/learn/ds ▶️ DTube ▶️ IPFS]]></description><link>http://direct.ecency.com/clustering/@romeokienzler/9adyh5we</link><guid isPermaLink="true">http://direct.ecency.com/clustering/@romeokienzler/9adyh5we</guid><category><![CDATA[clustering]]></category><dc:creator><![CDATA[romeokienzler]]></dc:creator><pubDate>Tue, 10 Apr 2018 05:45:21 GMT</pubDate><enclosure url="https://images.ecency.com/p/46aP2QbqUqBqwzwxM6L1P6uLNceBDDCM7FAciQeRgcu3dNURd9NDHTftua7v67bQSuUZC61V6xuJS8Gr82DfTvGiDsrr?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Introduction to Clustering with R-Studio on IoT vibration accelerometer sensor data]]></title><description><![CDATA[▶️ DTube ▶️ IPFS]]></description><link>http://direct.ecency.com/machinelearning/@romeokienzler/wuavr9o9</link><guid isPermaLink="true">http://direct.ecency.com/machinelearning/@romeokienzler/wuavr9o9</guid><category><![CDATA[machinelearning]]></category><dc:creator><![CDATA[romeokienzler]]></dc:creator><pubDate>Thu, 05 Apr 2018 14:01:00 GMT</pubDate><enclosure url="https://images.ecency.com/p/46aP2QbqUqBqwzwxM6L1P6uLNceBDDCMDSaz8nZ5XacZ4r6aBiWHMntLGr4z1mNM4C8ayE6YNif1y4XLYH1wwz8KBtDq?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[AI学习笔记——无监督学习之最大期望算法（Expectation-maximization）]]></title><description><![CDATA[上一篇文章介绍了无监督学习中最容易理解的经典算法K聚类。这个算法虽然简单但是有几个明显的缺点 1、首先要知道K的数值，也就是要确定有多少类 2、有时候可能只会得到局部最优(Local minimum)而得不到全局最优比如下图这种情况。 3、与K临近算法一样处理多维度问题的时候比较困难 4、缺少数学解释。]]></description><link>http://direct.ecency.com/cn/@hongtao/ai-expectation-maximization</link><guid isPermaLink="true">http://direct.ecency.com/cn/@hongtao/ai-expectation-maximization</guid><category><![CDATA[cn]]></category><dc:creator><![CDATA[hongtao]]></dc:creator><pubDate>Fri, 02 Feb 2018 15:10:54 GMT</pubDate><enclosure url="https://images.ecency.com/p/2gsjgna1uruvUuS7ndh9YqVwYGPLVszbFLwwpAYXZtFqJT2W5QgjbLNPDPREQbxZ2oTkoX7Cg5WXTbiD2tET2jA7rSQnjeom4xdHM5EsnGyrLGJ8az?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Clustering Text : An Introduction (robydnfs98)]]></title><description><![CDATA[What is Clustering? Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled]]></description><link>http://direct.ecency.com/writing/@robydnfs98/clustering-an-introduction</link><guid isPermaLink="true">http://direct.ecency.com/writing/@robydnfs98/clustering-an-introduction</guid><category><![CDATA[writing]]></category><dc:creator><![CDATA[robydnfs98]]></dc:creator><pubDate>Fri, 20 Oct 2017 16:17:21 GMT</pubDate><enclosure url="https://images.ecency.com/p/qjrE4yyfw5pQYiuVvgYiUBP16WHGGN7UNn1BCdGdziXA8tRXb7R6LSxpPKLLLinXYY4wFsyY4kkho3JEynsH2Hpj9wGgpY59nf9J73P47b5caDHhha66YSV3?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[What are some algorithms and data structures that every data scientist should know?]]></title><description><![CDATA[Depending on the purpose of application and quantity of data there is possible to make a first classification as follows. Clustering :is the problem of grouping the individuals in a population together]]></description><link>http://direct.ecency.com/algorithms/@alketcecaj/what-are-some-algorithms-and-data-structures-that-every-data-scientist-should-know</link><guid isPermaLink="true">http://direct.ecency.com/algorithms/@alketcecaj/what-are-some-algorithms-and-data-structures-that-every-data-scientist-should-know</guid><category><![CDATA[algorithms]]></category><dc:creator><![CDATA[alketcecaj]]></dc:creator><pubDate>Thu, 24 Aug 2017 09:38:48 GMT</pubDate></item><item><title><![CDATA[How to make VDOM IN FORTIGATE]]></title><description><![CDATA[In previous post we practice to make a firewall clustering with to different instance of Fortigates. In this post i want to describe how to make virtual firewall On this scenario. virtual firewall in Fortigaes]]></description><link>http://direct.ecency.com/fortigate/@hashem-s/how-to-make-vdom-in-fortigate</link><guid isPermaLink="true">http://direct.ecency.com/fortigate/@hashem-s/how-to-make-vdom-in-fortigate</guid><category><![CDATA[fortigate]]></category><dc:creator><![CDATA[hashem-s]]></dc:creator><pubDate>Mon, 24 Jul 2017 07:56:00 GMT</pubDate><enclosure url="https://images.ecency.com/p/FxX5caie56yqUbvo2DTJv1i6qm8z4ixTabBTrjodKSK8t9zDEhbgjo3b7ZSqBZ7yTWButrjhMfGNrNb67d3mKCLFK7VjdCYAE2ci2v9rSwYJ?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[SEO basics tutorial]]></title><description><![CDATA[SEO stands for Search Engine Optimization. It’s is mainly optimizing a website for search engines (Google, Yahoo, Bing and others): • planning and building up a site to rank well in web index comes about.]]></description><link>http://direct.ecency.com/keyword/@newpost/seo-basics-tutorial</link><guid isPermaLink="true">http://direct.ecency.com/keyword/@newpost/seo-basics-tutorial</guid><category><![CDATA[keyword]]></category><dc:creator><![CDATA[newpost]]></dc:creator><pubDate>Sat, 24 Jun 2017 15:15:24 GMT</pubDate><enclosure url="https://images.ecency.com/p/hgjbks2vRxvp3qu9YJoM7Fgojuu9uyf8MA8WHjqdboHtsXA4c3shFtMSDSc5uaReiFRVvQ953mXpjsDGuW9o9eqLnz?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[HF 19- A few thoughts now the euphoria is dying down some]]></title><description><![CDATA[Was it as bad as I feared. Not even close, at least at the start. The minnows were euphoric. Their power went up about 50-100x as my back of the envelope math suggested and as "Equality" and]]></description><link>http://direct.ecency.com/hardfork/@aggroed/hf-19-a-few-thoughts-now-the-euphoria-is-dying-down-some</link><guid isPermaLink="true">http://direct.ecency.com/hardfork/@aggroed/hf-19-a-few-thoughts-now-the-euphoria-is-dying-down-some</guid><category><![CDATA[hardfork]]></category><dc:creator><![CDATA[aggroed]]></dc:creator><pubDate>Thu, 22 Jun 2017 11:59:24 GMT</pubDate><enclosure url="https://images.ecency.com/p/2gsjgna1uruvUuS7ndh9YqVwYGPLVszbFLwwpAYXYqNf9ba4nD97JivC4B5N9wDb1arjrnq5q6Y8mc89ZBvtYYwq79V7SyyvLF91isJegYj9MjXPsk?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Pure Digital Technology, Announces Its Overall Business Approach]]></title><description><![CDATA[Pure Digital Technology, is focusing on novel computational systems in which flexible clustering and adaptive mass storage are pivotal concepts. Such systems can function both as routine components of]]></description><link>http://direct.ecency.com/clustering/@puredigital/pure-digital-technology-announces-its-overall-business-approach</link><guid isPermaLink="true">http://direct.ecency.com/clustering/@puredigital/pure-digital-technology-announces-its-overall-business-approach</guid><category><![CDATA[clustering]]></category><dc:creator><![CDATA[puredigital]]></dc:creator><pubDate>Sat, 03 Jun 2017 13:52:33 GMT</pubDate></item></channel></rss>