Tugas Kelompok Data Mining Ilmu Komputasi IK-36-GAB02 Data set : Mammographic Mass Data Set Damarsasi Cahyo Wilogo (1107120128) Farid Al Ghifari (1107120123) Ihsan Hasanudin (1107120117) Jeshurun Eliezer Cursoy (1107120040)
Views: 251 Damarsasi Wilogo
Updated version of Extraterrestrial technology on the Moon a.k.a. "Project Golden Dragon". Before you watch this video please read this message: This project is based on a personal "theory" about a major conspiracy by the US Department of Defense, a whistle-blower who left cryptographic clues and finally complete exposure of an obfuscated object found in the Zeeman crater. * * * This is no scientific build disclosure (yet). * * * This presentation is the result of 14 years of research; collecting 17Gb of data, analyzing and making enhancements using high-end forensic image enhancement software. I needed a very long time myself to look past the strange shape. Do not expect to see the machine immediately. Your eyes and mind need time to adapt so take your time and try not to think too hard of alien technology. You probably have an expectation what "technology on the Moon" may look like but this machine is unlike anything you have ever seen. In order to see it you must A) be willing to accept the impossible, B) free your mind and C) capable of "out of the box" thinking. Note: I cannot promise that you will see the alien technology. All depends on your willingness to accept that there is life outside Earth atmosphere. Only users who are fully open to extraterrestrial technology and who are able to watch this video without prejudice will have a better chance to comprehend the images. English is not my native language, so perhaps Roc Hatfield, author of 2 books "Ancient man On the Moon" and "Moon Base Cover Up?" may have a proper description of what to expect: "Our brains are not used to seeing Alien Technology so it might take a few minutes to see it. It is clearly a vast machine. I was able to make out a number of features after looking at it for a long time. It is made of thousands of inter-locking plates, like scales on an alligator. I believe this allows it to undulate like a snake or caterpillar. The machine is so long that it needs to flex in order to set flat on the lunar surface. If it was ridged it would be like a pencil on a basketball, both ends would be in the air when the middle was on the ground. By being flexible it can wrap it's huge length around the curvature of the Moon's surface. The machine is a mining crawler that takes in moon material at the front using a giant gantry and processes it on board in a massive on board factory. I believe the machine can fly and has been to earth in the past. Could be the dragons seen by ancient Chinese people. It looks like a dragon from the side". That really was a thorough description but please let me clarify something. Why do I call this machine a dragon? Because someone many centuries ago decided to call it that way. Why does it look like a dragon? Because someone many centuries ago decided that it should look that way. So is it a dragon? I do not know. If someone many centuries ago would have called it a rabbit, this machine now would be a rabbit. So, my dear friends, it is a just a name. Mr. Hatfield explained it very well why our machine looks the way it does and why it has these numeorus scales so before people start telling me that this is all fantasy watch the entire video before criticizing. Statistics show that very few really take time to watch the video. If you do not appreciate the voice, fine watch the much longer, original narrated version instead. Watching snippets of the video will only raise eyebrows as it does not show you the full story behind it. My conclusions are based on facts but they are non-scientific build and for those who decide to watch the video entirely, these viewers will understand that I did not build my theory entirely on a single gray-scale picture. I know that there are still a lot of unanswered questions for which I do not have a satisfying answer. After all I am no scientist and as I never wrote a paper in my life (probably never will) for me this personally is already some major achievement. From ALL your comments I will create a sequel and try to give throrough explanations but I might not have an answer for every question. I am still full of questions myself. I have no political agenda and I am only interested in the truth. Please note that I ban people and remove those comments that do not apply to the rules of this channel. The rules are listed in ABOUT. http://www.youtube.com/user/1967sander/about Finally: I am NOT doing this for making money: My Playback-based gross revenue is $0.00* Enjoy the presentation and spread the news! Make this disclosure to a success! A free downloadable ebook (epub) with several hundreds of images will be released 2017/2018! Greetz, Sander
Views: 6883982 1967sander
This tutorial is made by Center for Marketing Engineering, The Chinese University of Hong Kong. Objectives of Tutorial 1: 1. Learn basic skills of SPSS Modeler 2. Learn RFM Analysis of SPSS Modeler Data for master 2015: https://www.dropbox.com/s/rl9og69eef18t4t/SPSS%20Modeler%20Tutorial%201.zip?dl=0 Data for Undergraduate 2016: https://www.dropbox.com/s/tajyuwhj0usfdnk/SPSS%20Tutorial%20Fall%202016.rar?dl=0
Views: 30403 Marketing Engineering Center CUHK
This tutorial shows how to construct a predictive model using IBM SPSS Modeler. We use the Boston Housing dataset for our illustration. In addition, we also discuss how to evaluate the performance of the model using different nodes such as Graph Evaluation and Data Analysis Node. I hope you enjoy it and please let me know if you have any questions. Thanks for watching.
Views: 18637 IT_CHANNEL
This video is the first in a series offering insight into some under-used or little-used cool functions in SPSS Modeler software. This is particularly aimed at either 1) existing users (especially the self-taught) 2) people who have looked at/evaluated the software in the past 3) people who use similar software but are curious about stuff like this. In this first video you can learn about:- 1. The Data Audit Node: The ‘Swiss army knife’ of data cleaning 2. The ‘Generate’ menu: The super quick way to create new fields 3. The Filter Node: Zaps Awkward Fields 4. The Graphboard Node: A Visualisation Goldmine 5. The Simulation Nodes: Makes ‘What if’ analysis easy 6. The Reclassify Node: Cleaning categories has never been easier 7. The ‘Split’ Role: Separate models for separate groups
Views: 13973 Smart Vision Europe
Before you watch this video please read this message: This project is based on a personal "theory" about a major conspiracy by the US Department of Defense, a whistle-blower who left cryptographic clues and finally complete exposure of the obfuscated object found in the Zeeman crater. * * * This is no scientific build disclosure (yet). * * * What I show you is the result of 14 years of collecting data, analyzing and making image enhancements. I cannot promise you that you will see the alien object. I needed a long time myself to see the strange shape so do not expect to see it immediately. Take your time and try not to think of extraterrestrial technology when you watch. You probably already have an expectation what "technology on the Moon" looks like but it is unlike anything you ever seen, so clear your mind and try to "think out of the box". Roc Hatfield, author of 2 books "Ancient man On the Moon" and "Moon Base Cover Up?" may have a proper description: "Our brains are not used to seeing Alien Technology so it might take a few minutes to see it. It is clearly a vast machine. I was able to make out a number of features after looking at it for a long time. It is made of thousands of inter-locking plates, like scales on an alligator. I believe this allows it to undulate like a snake or caterpillar. The machine is so long that it needs to flex in order to set flat on the lunar surface. If it was ridged it would be like a pencil on a basketball, both ends would be in the air when the middle was on the ground. By being flexible it can wrap it's huge length around the curvature of the Moon's surface. The machine is a mining crawler that takes in moon material at the front using a giant gantry and processes it on board in a massive on board factory. I believe the machine can fly and has been to earth in the past. Could be the dragons seen by ancient Chinese people. It looks like a dragon from the side". My conclusions are based on facts (though non-scientific build) and there are still a lot of unanswered questions for which I do not have an answer. Also: I cannot promise you that you will see what I do, or as other people did. Either you see the alien technology or you dont. All I am offering is the truth. Nothing more. The full one hour interview on Las Vegas Radio is here: http://youtu.be/ciZtgW7JrKk Greetz, Sander
Views: 626040 1967sander
Here is a physics project we had to do. Hope u like it!!!!! I DO NOT OWN ANYTHING!!!!!! ALL MUSIC BELONGS TO ITS RIGHTFUL OWNERS!!! Resources: http://setas-www.larc.nasa.gov/CLEM/dspse.html http://nssdc.gsfc.nasa.gov/planetary/clementine.html http://solarsystem.nasa.gov/missions/profile.cfm?Target=Asteroids&MCode=Clementine http://lunar.gsfc.nasa.gov/gallery-historical.html htpps://llnl.gov/etr/pdfs/06_94.1pdf Song: Clementine Artist: Westlife
Views: 487 SoulStealerSlayers
Speaker: Tom Khabaza Tom will introduce the fundamentals of data mining: its applications, the process and the underlying principles, and apply this to data analysis tools. Using a “magic quadrant” for analytical tool design, Tom will overview a number of open and commercial tools, compare their strengths and weaknesses, and suggest how to place open data analysis tools at the top of the data mining heap. Bio: Tom Khabaza, sometimes called “the Isaac Newton of Data Mining” is the Founding Chairman of the Society of Data Miners. A data mining veteran of 25 years and many industries and applications, Tom helped create the world-leading Clementine data mining workbench (now called IBM SPSS Modeler) and the industry standard CRISP-DM analytics methodology, and led the first integrations of data mining and text mining. His recent thought leadership includes the 9 Laws of Data Mining and Predictive Analytics Strategy. ---- SUBSCRIBE to get the latest meetup videos: http://bit.ly/MVUKSubscribe ---- Video by Meetupvideo ( https://www.meetupvideo.com )
Views: 256 Meetupvideo United Kingdom
In this tutorial, I will show you how to construct and Classification and Regression Tree (CART) for data mining purposes. We show through example of bank loan application dataset. We then will show steps to explore and interpret the constructed tree. I hope that help and let me know if you have any questions. Thanks.
Views: 17297 IT_CHANNEL
Naive Bayes Classifier- Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 122651 Augmented Startups
How did the U.S. steal a sunken nuclear submarine? Right out from under the nose of the Soviet Union... Subscribe now: http://bit.ly/DarkDocs Cool thumb details: The Hughes Mining Barge, or HMB-1, is a submersible barge about 99 m (324 ft) long, 32 m (106 ft) wide, and more than 27 m (90 ft) tall. The HMB-1 was originally developed as part of Project Azorian (more widely, but erroneously, known as "Project Jennifer"), the top-secret effort mounted by the Central Intelligence Agency to salvage the remains of the Soviet submarine K-129 from the ocean floor. The HMB-1 was designed to allow the device that would be used to grasp and lift the submarine to be constructed inside the barge and out of sight, and to be installed in the Glomar Explorer in secrecy. This was done by towing the HMB-1, with the capture device inside, to a location near Catalina Island (off the coast of California), and then submerging it onto stabilizing piers that had been installed on the seafloor. The Glomar Explorer was then maneuvered over the HMB-1, the retractable roof was opened, and the capture device lifted into the massive "moon pool" of the ship, all within clear sight of people on the beach ------- DarkDocs is a new narrated documentary video from Dark5 taking an in-depth look at at some of the most mysterious stories on Earth. This week: Project Azorian, the Glomar Explorer, and the mysterious CIA to recover the lost Soviet submarine, K-129. It’s March 1968. An unexplained event causes a Soviet Golf-II submarine known as the K-129 to sink to the bottom of the Pacific Ocean while enroute to its patrol station off the coast of Hawaii. Publicly, no one in the world knew what happened, where the submarine was, or what secrets might be hidden on board. Behind the scenes, however, reporters caught wind of a classified US government operation to pry the wreckage from the floor with a giant claw and uncover whatever secret technology might be hidden within. The mission was codenamed Project Azorian, and it was launched from a covert ship named the Glomar Explorer. Pressed for comment on the operation, a US government spokesman flatly replied, [quote] “We can neither confirm nor deny the existence of the information requested but, hypothetically, if such data were to exist, the subject matter would be classified, and could not be disclosed…"
Views: 684447 Dark Docs
-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 649 Suyeon Jung
Part 2 - Predictive analytics within customer intimacy. What is predictive analytics, and how understand the data mining and results from IBM SPSS Modeler. There is also a demo of basket analysis that is used for cross selling of products.
Views: 907 grudander
Si deseas más información entra aquí: http://www.nexolution.com/
Views: 2705 Nexolution BA
Taller de Mineria de Datos Clementa y Datamine Studio 3 con base de datos SQL Server
Views: 324 Iza Cintya
Pour ce nouvel épisode des Décideurs De l'Emploi consacré aux métiers du Big-Data, notre journaliste Pierre Chavanne reçoit Thibaut Portal, Vice-Président Services & Operations chez Makazi, et Benjamin Stanislas, psychosociologue et consultant au sein du cabinet de recrutement Clémentine ! On résume souvent le Big-Data par ces trois « V » : Volume, Vélocité, Variété ! En clair, cela signifie que l'on assiste aujourd'hui à une multiplication quantitative de données variées que l'on sait exploiter de façon rapide. Ces évolutions donnent naissance à trois enjeux majeurs : il faut stocker, analyser et exploiter ces données ! Pour cela, de nouveaux métiers apparaissent... Profils recherchés, formations, débouchés, rémunération... Nos deux experts répondent à toutes vos questions sur ce sujet !
Views: 21715 DECIDEURSTV
Découvrez les services de Clementine, Votre Expert-Comptable en Ligne !
Views: 1104 Compta Clementine
Data preparation takes a lot of time in a data mining project. The performance of regular expressions and split string functions are compared when parsing text strings for values relevant to a data mining project using STATISTICA Data Miner. Data and STATISTICA Visual Basic code are available at DManswers.com
Views: 578 DManswers
如何針對某議題進行資料分析? 資料量過於龐大時該怎麼辦? 資料探勘(Data Minimg)，可幫助你從大量多面向的資料中挖掘金礦。本課程針對『關聯規則技術、分類、聚類』此三大議題進行教學，幫助你更有效做出商業決策!! +更多資料處理與分析課程，請來TibaMe: https://www.tibame.com/q?pg=oocourses_all&tg=OOCourseAll-listCourses&cx=22.20000&cmd=search&viewMode=default&categoryFilter=30000000034
Views: 3501 TibaMe
For more info on the 2nd Off Earth Mining Forum see http://www.acser.unsw.edu.au/oemf2015 In 1995, LtCol Dale Tietz and Dr. Bill Stone reviewed the data from the Clementine satellite mission for the first time together. Tietz, who had directed space-based portions of the Pentagon's Strategic Defense Initiative (SDI) program, pointed out the strong possibility that constituents of water were detected by bistatic radar returns at the South Pole of the Moon––specifically at Shackleton crater. NASA and SDI scientists were ecstatic! In the Clementine and Lunar Prospector data Stone realized––that with unlimited quantities of lunar ice in the deep, cold trap craters––the opportunity to generate large quantities of rocket propellants at Shackleton and Low Earth Orbit (LEO) to refuel vehicles in cis-lunar space. The real moneymaker was to ship raw water to LEO for processing into liquid oxygen/liquid hydrogen propellants to then sell at significantly reduced prices than anything available on Earth--on demand--to all space farers. The advent, beginning in the fall of 2004, of a viable and burgeoning space tourism industry bent on delivering paying passengers to eventual hotels in LEO and beyond, convinced the team that it was finally time to go public with the concept. In March of 2007, Stone discussed the topic at the TED conference in Monterey, California along side Richard Branson. In 2008 Shackleton Energy Company was founded with Jim Keravala who expanded the project into a full space infrastructure program. The SEC Business Plan is complete and we are now in discussions with investors throughout the world. --- Jim comes to SEC with an exemplary 25 year background in space development and operations. Instrumental in the launch of over a dozen satellites, he was also involved in the establishment of new space programs for emerging space nations. Jim combines systems engineering and entrepreneurial drive to build long term infrastructure vision based upon near term capital requirements in his role as architect of SEC's program. Jim also leads SEC's Middle Eastern, European and Russian operations teams.
Views: 113 ACSER UNSW