Data visualization: global view of blog posts relationship

As stated on a post where i talked about using tf-idf to detect similarity between two blog posts, my blog is just a bunch of posts sorted by date, no category, no fancy features like user interest tracking, post ranking, etc. I usually work on many different domains (robotic, IoT, backend, frontend platform design, etc.), so my posts are mixed up between these domains. This may be difficult for readers who want to follow up their interesting topic on my blog.

So what is a good strategy for navigating between posts on a blog ?

Simple (naive) document clustering using tf-idf and k-mean

When i developed this blog (using my own client-server platform such as web server, back-end, front-end, etc., built from ash/scratch :) ), i simply designed it as a simple "note book" where i put my ideas or some stuffs that i have done. So, initially, there are no category no advance feature like post suggestion based on current post, etc. It is just a bunch of posts sorting by date. The thing is, i usually work on many different domains (robotic, IoT, backend, frontend platform design, etc.), so my posts are mixed up between different categories. It is fine for me, but is a real inconvenience for readers who want to follow up their interesting category on the blog. Of course, i could redesign the blog and add the missing features by messing around with the relational database design (i'm using SQLite btw), manually classifying the posts in the back-end, etc. But, i'm a kind of lazy people, so i've been thinking of a more automatic solution. How about an automatic document clustering feature based on a data mining approach ? Here we go!

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