I'm just wondering why my article has gained no interest or popularity ?
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If you are concerned about votes, perhaps it will take some more time.
Thanks for your reply. The number of votes normally indicates an article's popularity. :)
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Finally, I've re-composed the article's title: "
Quote:
How To Implement The AI Supervised Learning K-Means Clustering Algorithm And Use It For Solving Data Classification Problems"
So, what's about this one?
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Definitely peaks my interest more :thumbsup: CodeProject is gonna cut off the title on listings somewhere around the middle but all the important keywords are in the first half anyways.
Yes of course. I'll compact the beginning of the article's title right now.
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Yes of course. I'll compact the beginning of the article's title right now.
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Definitely peaks my interest more :thumbsup: CodeProject is gonna cut off the title on listings somewhere around the middle but all the important keywords are in the first half anyways.
Finally, I've corrected the title :)
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Actually I realized you'd have to change "Use" to "Using" if you did that which doesn't really save much space. English - ain't it fun? Could just use "Implement" though in the imperative sense.
Excuse me, but if I don't understand which word I should use in the imperative sense "Implement" or "Use", or both?? :)
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Excuse me, but if I don't understand which word I should use in the imperative sense "Implement" or "Use", or both?? :)
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Actually I realized you'd have to change "Use" to "Using" if you did that which doesn't really save much space. English - ain't it fun? Could just use "Implement" though in the imperative sense.
Here's a couple of variants of the same title: Implement The AI Supervised Learning K-Means Clustering Algorithm And Use It For Solving Data Classification Problems Implementing the AI Supervised Learning K-Means Clustering Algorithm and Be Using It for Solving Data Classification Problems Which one seems to you the most correct?
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Here's a couple of variants of the same title: Implement The AI Supervised Learning K-Means Clustering Algorithm And Use It For Solving Data Classification Problems Implementing the AI Supervised Learning K-Means Clustering Algorithm and Be Using It for Solving Data Classification Problems Which one seems to you the most correct?
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Thanks for the guidance. :)
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Thanks for the guidance. :)
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You're very welcome. :)
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Recently, I published an article A Gentle Introduction To Optimal K-Means Clustering, and I'm just wondering why my article has gained no interest or popularity? I've reverse-engineered an article published at Analytics Vidhya (The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need) by providing an even more a detailed explanation of the k-means clustering algorithm. Finally, I'd like to know if this article requires to be re-worked. Can you guide me please, what's wrong with this article?
I like turtles.
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Recently, I published an article A Gentle Introduction To Optimal K-Means Clustering, and I'm just wondering why my article has gained no interest or popularity? I've reverse-engineered an article published at Analytics Vidhya (The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need) by providing an even more a detailed explanation of the k-means clustering algorithm. Finally, I'd like to know if this article requires to be re-worked. Can you guide me please, what's wrong with this article?
90% of developers are toddlers playing with constructions set and din't involve themselves with math, algorithms or actual programming.
GCS d--(d+) s-/++ a C++++ U+++ P- L+@ E-- W++ N+ o+ K- w+++ O? M-- V? PS+ PE- Y+ PGP t+ 5? X R+++ tv-- b+(+++) DI+++ D++ G e++ h--- r+++ y+++* Weapons extension: ma- k++ F+2 X
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90% of developers are toddlers playing with constructions set and din't involve themselves with math, algorithms or actual programming.
GCS d--(d+) s-/++ a C++++ U+++ P- L+@ E-- W++ N+ o+ K- w+++ O? M-- V? PS+ PE- Y+ PGP t+ 5? X R+++ tv-- b+(+++) DI+++ D++ G e++ h--- r+++ y+++* Weapons extension: ma- k++ F+2 X
Thanks for your reply.
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Recently, I published an article A Gentle Introduction To Optimal K-Means Clustering, and I'm just wondering why my article has gained no interest or popularity? I've reverse-engineered an article published at Analytics Vidhya (The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need) by providing an even more a detailed explanation of the k-means clustering algorithm. Finally, I'd like to know if this article requires to be re-worked. Can you guide me please, what's wrong with this article?
You talk about what and how; but you don't say "why" this subject should be of interest. You need a "hook" to grab the readers' attention … early. Think of something in real life the audience can relate to and how your article / topic relates.
It was only in wine that he laid down no limit for himself, but he did not allow himself to be confused by it. ― Confucian Analects: Rules of Confucius about his food
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It's statistics. Outside of a few weirdos, statistics is something people only do if coerced.
Real programmers use butterflies
Guilty. I've alays been a weirdo CQ de W5ALT
Walt Fair, Jr.PhD P. E. Comport Computing Specializing in Technical Engineering Software
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I've already modified the title of my article: Implementing An Optimal K-Means Clustering Algorithm. So, how's that?
Why not a simple title, like "Classifying Data Using Artificial Intelligence"? And maybe the blurb can be something like, "A simple introduction to using K-Means Clustering for data analysis," or something like that. Then make the first paragraph about types of data and use cases, to draw the reader in to how to do it with K-Means Clustering. No need to scare your users by using big words in the title. The current title tells me it is going to be technical and non-user friendly except for possibly those who are already knowledgeable in the field.
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Why not a simple title, like "Classifying Data Using Artificial Intelligence"? And maybe the blurb can be something like, "A simple introduction to using K-Means Clustering for data analysis," or something like that. Then make the first paragraph about types of data and use cases, to draw the reader in to how to do it with K-Means Clustering. No need to scare your users by using big words in the title. The current title tells me it is going to be technical and non-user friendly except for possibly those who are already knowledgeable in the field.
Thanks for valuable comment. :) I've already reworked the title of my article, based on your recommendations.
