- Mar 2021
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www.bmj.com www.bmj.com
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Clift, Ash K., Carol A. C. Coupland, Ruth H. Keogh, Karla Diaz-Ordaz, Elizabeth Williamson, Ewen M. Harrison, Andrew Hayward, et al. ‘Living Risk Prediction Algorithm (QCOVID) for Risk of Hospital Admission and Mortality from Coronavirus 19 in Adults: National Derivation and Validation Cohort Study’. BMJ 371 (20 October 2020): m3731. https://doi.org/10.1136/bmj.m3731.
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Szabelska, A., Pollet, T. V., Dujols, O., Klein, R. A., & IJzerman, H. (2021). A Tutorial for Exploratory Research: An Eight-Step Approach. PsyArXiv. https://doi.org/10.31234/osf.io/cy9mz
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- Feb 2021
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advances.sciencemag.org advances.sciencemag.org
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Matrajt, Laura, Julia Eaton, Tiffany Leung, und Elizabeth R. Brown. „Vaccine Optimization for COVID-19: Who to Vaccinate First?“ Science Advances 7, Nr. 6 (1. Februar 2020): eabf1374. https://doi.org/10.1126/sciadv.abf1374.
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- Jan 2021
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twitter.com twitter.com
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NetScience[@net_science] (2020-08-23) Pseudoscientific Content on YouTube: Assessing the Effects of Watch History on the Recommendation Algorithm. (arXiv:2010.11638v1 [http://cs.CY]) http://arxiv.org/abs/2010.11638. Twitter.Retrieved from: https://twitter.com/SciBeh/status/1319564893503225856
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- Dec 2020
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dl.acm.org dl.acm.org
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; e-commerce sites have an economic incentiveto use personalization to induce users into spending moremoney
personalize of algorithm can make people spend more money, which help to improve the benifical of both website and merchant.
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- Nov 2020
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psyarxiv.com psyarxiv.com
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Pennycook, G., & Rand, D. (2020). The Cognitive Science of Fake News. PsyArXiv. https://doi.org/10.31234/osf.io/ar96c
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- Sep 2020
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Kojaku, S., Livan, G., & Masuda, N. (2020). Detecting citation cartels in journal networks. ArXiv:2009.09097 [Physics]. http://arxiv.org/abs/2009.09097
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www.scientificamerican.com www.scientificamerican.com
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Hotz, J. (n.d.). Can an Algorithm Help Solve Political Paralysis? Scientific American. Retrieved September 21, 2020, from https://www.scientificamerican.com/article/can-an-algorithm-help-solve-political-paralysis/
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- Aug 2020
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www.nature.com www.nature.com
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Shi, W., Wang, L., & Qin, J. (2020). Extracting user influence from ratings and trust for rating prediction in recommendations. Scientific Reports, 10(1), 13592. https://doi.org/10.1038/s41598-020-70350-1
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www.youtube.com www.youtube.com
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Identifying social media manipulation with OSoMe tools. (2020, August 11). https://www.youtube.com/watch?v=1BMv0PrdVGs&feature=youtu.be
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advances.sciencemag.org advances.sciencemag.org
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Fischer, E. P., Fischer, M. C., Grass, D., Henrion, I., Warren, W. S., & Westman, E. (2020). Low-cost measurement of facemask efficacy for filtering expelled droplets during speech. Science Advances, eabd3083. https://doi.org/10.1126/sciadv.abd3083
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- Jul 2020
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Cazabet, R., Boudebza, S., & Rossetti, G. (2020). Evaluating Community Detection Algorithms for Progressively Evolving Graphs. ArXiv:2007.08635 [Physics]. http://arxiv.org/abs/2007.08635
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- Jun 2020
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Ben-David, S. (2018). Clustering—What Both Theoreticians and Practitioners are Doing Wrong. ArXiv:1805.08838 [Cs, Stat]. http://arxiv.org/abs/1805.08838
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psyarxiv.com psyarxiv.com
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Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2019, December 4). Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools. https://doi.org/10.31234/osf.io/ky4x8
Tags
- decision autonomy
- attention economy
- online behavior
- cognitive tools
- boosting
- digital
- misinformation
- online manipulation
- technocognition
- disinformation
- AI
- fake news
- is:preprint
- self-nudging
- lang:en
- choice architecture
- artificial intelligence
- behavioral policy
- decision aid
- reasoning
- nudging
- internet
- algorithm
Annotators
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Jazayeri, A., & Yang, C. C. (2020). Motif Discovery Algorithms in Static and Temporal Networks: A Survey. ArXiv:2005.09721 [Physics]. http://arxiv.org/abs/2005.09721
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Seetharaman, J. H. and D. (2020, May 26). Facebook Executives Shut Down Efforts to Make the Site Less Divisive. Wall Street Journal. https://www.wsj.com/articles/facebook-knows-it-encourages-division-top-executives-nixed-solutions-11590507499
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Costantini, S., De Meo, P., Giorgianni, A., Migliorato, V., Provetti, A., & Salvia, F. (2020). Exploring Low-degree Nodes First Accelerates Network Exploration. ArXiv:2005.08050 [Physics]. http://arxiv.org/abs/2005.08050
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www.tandfonline.com www.tandfonline.com
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Efron, B. (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, 115(530), 636–655. https://doi.org/10.1080/01621459.2020.1762613
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journals.sagepub.com journals.sagepub.com
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Rosenbusch, H., Hilbert, L. P., Evans, A. M., & Zeelenberg, M. (2020). StatBreak: Identifying “Lucky” Data Points Through Genetic Algorithms. Advances in Methods and Practices in Psychological Science, 2515245920917950. https://doi.org/10.1177/2515245920917950
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Mariani, M. S., & Lü, L. (2020). Network-based ranking in social systems: Three challenges. Journal of Physics: Complexity, 1(1), 011001. https://doi.org/10.1088/2632-072X/ab8a61
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- May 2020
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apolitical.co apolitical.co
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7 emerging themes in digital government. (2020, May 26). https://apolitical.co/en/solution_article/7-emerging-themes-in-digital-government
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Lanovaz, M., & Turgeon, S. (2020). Tutorial: Applying Machine Learning in Behavioral Research [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9w6a3
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github.com github.com
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Deepset-ai/haystack. (2020). [Python]. deepset. https://github.com/deepset-ai/haystack (Original work published 2019)
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arxiv.org arxiv.org
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Tuninetti, M., Aleta, A., Paolotti, D., Moreno, Y., & Starnini, M. (2020). Prediction of scientific collaborations through multiplex interaction networks. ArXiv:2005.04432 [Physics]. http://arxiv.org/abs/2005.04432
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misinforeview.hks.harvard.edu misinforeview.hks.harvard.edu
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Nsoesie, E. O., & Oladeji, O. (2020). Identifying patterns to prevent the spread of misinformation during epidemics. Harvard Kennedy School Misinformation Review, COVID-19 and Misinformation. https://doi.org/10.37016/mr-2020-014
Tags
- is:article
- lang:en
- prevention
- vaccination
- healthcare
- public health
- COVID-19
- treatment
- misinformation
- spread
- transmission
- algorithm
Annotators
URL
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dl.acm.org dl.acm.org
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Epstein, Z., Pennycook, G., & Rand, D. (2020). Will the Crowd Game the Algorithm? Using Layperson Judgments to Combat Misinformation on Social Media by Downranking Distrusted Sources. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–11. https://doi.org/10.1145/3313831.3376232
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- Apr 2020
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covidscholar.org covidscholar.org
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COVIDScholar. (n.d.). Retrieved April 29, 2020, from https://covidscholar.org/
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arxiv.org arxiv.org
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Kerzendorf, W. E., Patat, F., Bordelon, D., van de Ven, G., & Pritchard, T. A. (2020). Distributed peer review enhanced with natural language processing and machine learning. Nature Astronomy. https://doi.org/10.1038/s41550-020-1038-y
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Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679
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- Jan 2020
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socialmediacollective.org socialmediacollective.org
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- Oct 2019
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runestone.academy runestone.academy
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An algorithm is a step by step list of instructions that if followed exactly will solve the problem under consideration
Là 1 danh sách hướng dẫn chính xác từng bước được dùng để giải quyết vấn đề.
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- Jul 2019
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www.flowerbrackets.com www.flowerbrackets.com
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Java program find GCD and LCM of two numbers using euclid’s algorithm
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- May 2019
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engl201.opened.ca engl201.opened.ca
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enginethatistheproblembut,rather,theusersofsearchengineswhoare.Itsuggeststhatwhatismostpopularissimplywhatrisestothetopofthesearchpile
- I wanted to highlight the previous sentence as well, but for some reason it wouldn't let me*
I understand why the author is troubled by the campaign's opinion of "It's not the search engines fault". It makes it seem as if there was nothing that could be done to stop promoting those ideas, and that if something is popular it will just have to be the result at the top.
This can be problematic, as people who were not initially searching that specific phrase may click through to read racist, sexist, homophobic, or biased information (to just name a few) that perpetuates inaccuracies and negative stereotypes. It provides easier access into dangerous thinking built on the foundations of racism, sexism, etc.
If the algorithms are changed or monitored to remove those negative searches, the people exposed to those ideas would decrease, which could help tear down the extreme communities that can build up from them.
While I do understand this view, I also think that system can be helpful too. All the search engine does is reflect the most popular searches, and if negative ideals are what people are searching, then we can become aware and direct their paths to more educational and unbiased sources. It could be interesting to see what would happen if someone clicked on a link that said "Women belong in the kitchen", that led them to results that spoke about equality and feminism.
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- Mar 2019
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vidooly.com vidooly.com
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When Instagram found out that users missed out on 70% of the posts on their feed, they announced a new algorithm. An algorithm that promised to let you see the posts that you care about the most. But the workings of the algorithm remained a burning question until recently when the Facebook-owned company revealed how does the Instagram algorithm work. When the Instagram feed was tweaked from chronological to algorithmic, people anticipated a drop in engagement levels. However, people have now started seeing 90% of their feed since the algorithm was instituted. Read on to find out everything you need to know about the Instagram feed algorithm.
When Instagram found out that users missed out on 70% of the posts on their feed, they announced a new algorithm. An algorithm that promised to let you see the posts that you care about the most.
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- Jan 2019
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www.maritime-executive.com www.maritime-executive.com
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Contrary to mainstream thinking that this new technology is unregulated, it’s really quite the opposite. These systems apply the strictest of rules under highly deterministic and predictable models that are regulated through mathematics. In the future, industry will be regulated not just by institutions and committees but by algorithms and mathematics. The new technology will gradually out-regulate the regulators and, in many cases, make them obsolete because the new system offers more certainty. Antonopoulos explains that “the opposite of authoritarianism is not chaos, but autonomy.”
<big>评:</big><br/><br/>1933 年德国包豪斯设计学院被纳粹关闭,大部分师生移民到美国,他们同时也把自己的建筑风格带到了美利坚。尽管人们在严格的几何造型上感受到了冷漠感,但是包豪斯主义致力于美术和工业化社会之间的调和,力图探索艺术与技术的新统一,促使公众思考——「如何成为更完备的人」?而这一点间接影响到了我们现在所熟知的美国式人格。<br/><br/>区块链最终会超越「人治」、达到「算法自治」的状态吗?类似的讨论声在人工智能领域同样不绝于耳。「绝对理性」站到了完备人格的对立面,这种冰冷的特质标志着人类与机器交手后的败退。过去有怀疑论者担心,算法的背后实际上由人操控,但随着「由算法生成」的算法,甚至「爷孙代自承袭」算法的出现,这样的担忧逐渐变得苍白无力——我们有了更大的焦虑:是否会出现 “blockchain-based authoritarianism”?
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- Nov 2018
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logicmag.io logicmag.io
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how does misrepresentative information make it to the top of the search result pile—and what is missing in the current culture of software design and programming that got us here?
Two core questions in one? As to "how" bad info bubbles to the top of our search results, we know that the algorithms are proprietary—but the humans who design them bring their biases. As to "what is missing," Safiya Noble suggests here and elsewhere that the engineers in Silicon Valley could use a good dose of the humanities and social sciences in their decision-making. Is she right?
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- Jul 2018
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theoutline.com theoutline.com
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The new organs process this enormous amount of information to break you down into categories, which are sometimes innocuous like, “Listens to Spotify” or “Trendy Moms”, but can also be more sensitive, identifying ethnicity and religious affiliation, or invasively personal, like “Lives away from family”. More than this, the new organs are being integrated with increasingly sophisticated algorithms, so they can generate predictive portraits of you, which they then sell to advertisers who can target products that you don’t even know you want yet.
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www.npr.org www.npr.org
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Perelman says his Babel Generator also proves how easy it is to game the system. While students are not going to walk into a standardized test with a Babel Generator in their back pocket, he says, they will quickly learn they can fool the algorithm by using lots of big words, complex sentences, and some key phrases - that make some English teachers cringe. "For example, you will get a higher score just by [writing] "in conclusion,'" he says.
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"The idea is bananas, as far as I'm concerned," says Kelly Henderson, an English teacher at Newton South High School just outside Boston. "An art form, a form of expression being evaluated by an algorithm is patently ridiculous."
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- Apr 2018
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www.theatlantic.com www.theatlantic.com
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This fall, my colleagues and I released gobo.social, a customizable news aggregator. Gobo presents you with posts from your friends, but also gives you a set of sliders that govern what news you see and what’s hidden from you. Want more serious news, less humor? Move a slider. Need to hear more female voices? Adjust the gender slider, or press the “mute all men” button for a much quieter internet. Gobo currently includes half a dozen ways to tune your news feed, with more to come.
Gobo, a proof of concept.
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- Mar 2018
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github.com github.com
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Distance from a point to a line segment
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- Mar 2017
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en.wikipedia.org en.wikipedia.org
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for not very large numbers
Would an approach using the Sieve or Eratosthenes work better for very large numbers? Or the best shot would be a probabilistic primality test?
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- Dec 2016
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en.wikipedia.org en.wikipedia.org
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the Closest pair problem
最近点对,算法导论中的经典算法
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Annotators
URL
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- Oct 2016
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www.theguardian.com www.theguardian.com
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Machine learning:
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- Aug 2016
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www.nytimes.com www.nytimes.com
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student-directed learning system
How can they say student-directed. It's a Facebook algorithm.
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- Apr 2016
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cssanalytics.wordpress.com cssanalytics.wordpress.com
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While there are assets that have not been assigned to a cluster If only one asset remaining then Add a new cluster Only member is the remaining asset Else Find the asset with the Highest Average Correlation (HC) to all assets not yet been assigned to a Cluster Find the asset with the Lowest Average Correlation (LC) to all assets not yet assigned to a Cluster If Correlation between HC and LC > Threshold Add a new Cluster made of HC and LC Add to Cluster all other assets that have yet been assigned to a Cluster and have an Average Correlation to HC and LC > Threshold Else Add a Cluster made of HC Add to Cluster all other assets that have yet been assigned to a Cluster and have a Correlation to HC > Threshold Add a Cluster made of LC Add to Cluster all other assets that have yet been assigned to a Cluster and have Correlation to LC > Threshold End if End if End While
Fast Threshold Clustering Algorithm
Looking for equivalent source code to apply in smart content delivery and wireless network optimisation such as Ant Mesh via @KirkDBorne's status https://twitter.com/KirkDBorne/status/479216775410626560 http://cssanalytics.wordpress.com/2013/11/26/fast-threshold-clustering-algorithm-ftca/
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- Nov 2015
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courses.edx.org courses.edx.org
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I don't totally agree with the fact that writers the creation of language is the target of a writer, I think language is just a means, the "algorithm" that "plays" with words/semanthincs, as any machine can do
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