I'm just wondering why my article has gained no interest or popularity ?
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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've just looked at the stats for your article. It shows that you had a peak of a little over 800 people looking at it in one day. Since then, there has been a steady decline. So why was there a peak? There would have been a peak because the article was visible on the home page at this point so it was something that was "in your face" for people coming in to the site. Given site traffic, why has this not been higher? If you think of the home page as being like a shop front, the title and description you give your article acts as the packaging. If the packaging isn't eye catching, people aren't going to bother with it. Your title needs to scream "look at me, I'm something interesting" and your description needs to tell people, "this is what I'm about and this is why YOU NEED to know about me". If you don't catch the readers attention instantly, you are competing with all the other articles on the home page. Once the article falls off the home page, your title, description, categorisation and tags become vital.
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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?
It's statistics. Outside of a few weirdos, statistics is something people only do if coerced.
Real programmers use butterflies
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It's statistics. Outside of a few weirdos, statistics is something people only do if coerced.
Real programmers use butterflies
Just like parsers, lexers and regex :laugh:
Best, Sander sanderrossel.com Migrating Applications to the Cloud with Azure arrgh.js - Bringing LINQ to JavaScript Object-Oriented Programming in C# Succinctly
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I saw the article when it was published, did a quick glance and figured out its not for me or interest. As for Article, there is nothing wrong or needs to be reworked.
cheers,
Super
------------------------------------------ Too much of good is bad,mix some evil in it
I second you, super! It looks like a great article, but it's just not a subject that I need to read about. Can't find anything wrong with it either.
Anything that is unrelated to elephants is irrelephant
Anonymous
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The problem with quotes on the internet is that you can never tell if they're genuine
Winston Churchill, 1944
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Never argue with a fool. Onlookers may not be able to tell the difference.
Mark Twain -
Just like parsers, lexers and regex :laugh:
Best, Sander sanderrossel.com Migrating Applications to the Cloud with Azure arrgh.js - Bringing LINQ to JavaScript Object-Oriented Programming in C# Succinctly
I won a best article prize for my last parser there, clever guy. ;P
Real programmers use butterflies
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I won a best article prize for my last parser there, clever guy. ;P
Real programmers use butterflies
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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?
Thanks a lot, the community for your valuable comments and my question replies. :)
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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?
In my opinion, when you use what an article is literally about in the title you'll only attract the type of people interested in that subject. I see "optimal k-means clustering" and I think "that sounds interesting but complex. Don't have time, might bookmark for a rainy day." So for traffic, it is probably a better idea to use the title to explain what this article practically accomplishes and maybe include the details in the sub-header description. So instead of "A Gentle Introduction to Optimal K-Means Clustering" something like "A Gentle Introduction to Multi-Objective AIs" with the sub-header "An optimal k-means clustering algorithm". That grabs my attention more. A multi-purpose AI? Hmmm, that might be useful in -insert project or situation here-. Such targeted articles still won't generate the traffic of more general-purpose stuff but I know from my own habits that if I know why an article is useful I'm much more likely to read it even if I don't fully understand the topics because I now have a motivation to invest at least some time in doing the background research to understand those topics at a basic level :thumbsup:
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In my opinion, when you use what an article is literally about in the title you'll only attract the type of people interested in that subject. I see "optimal k-means clustering" and I think "that sounds interesting but complex. Don't have time, might bookmark for a rainy day." So for traffic, it is probably a better idea to use the title to explain what this article practically accomplishes and maybe include the details in the sub-header description. So instead of "A Gentle Introduction to Optimal K-Means Clustering" something like "A Gentle Introduction to Multi-Objective AIs" with the sub-header "An optimal k-means clustering algorithm". That grabs my attention more. A multi-purpose AI? Hmmm, that might be useful in -insert project or situation here-. Such targeted articles still won't generate the traffic of more general-purpose stuff but I know from my own habits that if I know why an article is useful I'm much more likely to read it even if I don't fully understand the topics because I now have a motivation to invest at least some time in doing the background research to understand those topics at a basic level :thumbsup:
I've already modified the title of my article: Implementing An Optimal K-Means Clustering Algorithm. So, how's that?
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It's statistics. Outside of a few weirdos, statistics is something people only do if coerced.
Real programmers use butterflies
Hey, I resemble that.
Wrong is evil and must be defeated. - Jeff Ello
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I've already modified the title of my article: Implementing An Optimal K-Means Clustering Algorithm. So, how's that?
The primary subject is still "Optimal K-Means Clustering Algorithm" which before I clicked and read a bit of your article because of this post, I had zero idea what on Earth that even was. If I was busy and skimming for articles to read later from the front page I would probably pass to be honest. I think if you somehow hint in the title it relates to AI you'd get way more interest. Edit: Of course I don't really do AI stuff, so maybe that's a common algorithm? This is all just my perspective as an AI noob ;P
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The primary subject is still "Optimal K-Means Clustering Algorithm" which before I clicked and read a bit of your article because of this post, I had zero idea what on Earth that even was. If I was busy and skimming for articles to read later from the front page I would probably pass to be honest. I think if you somehow hint in the title it relates to AI you'd get way more interest. Edit: Of course I don't really do AI stuff, so maybe that's a common algorithm? This is all just my perspective as an AI noob ;P
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I think if you somehow hint in the title it relates to AI you'd get way more interest.
I'm just working on it. Specifically, K-Means clustering is a very popular AI Machine Learning algorithm, I've already discussed in my previous articles years ago. Anyway, I'll try to change the title to something AI and machine learning related. :)
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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?
If you are concerned about votes, perhaps it will take some more time.
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Quote:
I think if you somehow hint in the title it relates to AI you'd get way more interest.
I'm just working on it. Specifically, K-Means clustering is a very popular AI Machine Learning algorithm, I've already discussed in my previous articles years ago. Anyway, I'll try to change the title to something AI and machine learning related. :)
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You're saying my binding spell worked, then. That's not against the rules. I read them.
Real programmers use butterflies
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It's a really good article from the parts I understand :thumbsup: I didn't notice it mentioned but what's the Big-Oh performance of the sub-optimal k-means clustering algorithm? O(k)?
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I didn't notice it mentioned but what's the Big-Oh performance of the sub-optimal k-means clustering algorithm? O(k)? Quote Selected Text
The complexity of the sub-optimal (!) k-means algorithm is typically very high (e.g. it's NP-hard). That's actually why I've used the number of algorithm optimizations such as k-means++ initialization algorithm, thoroughly discussed in this article, as well as the ability of the initialization process to produce the number of initial clusters prior to performing the actual clustering, and that really helps to reduce the computational complexity of the k-means algorithm.
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It's a really good article from the parts I understand :thumbsup: I didn't notice it mentioned but what's the Big-Oh performance of the sub-optimal k-means clustering algorithm? O(k)?
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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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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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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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.
