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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
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
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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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.