Implicit kd-tree

Okay, so imagine you have a bunch of toys to keep in your toy box. But you want to keep them sorted so you can find them easily later.

So, you decide to sort them based on their size. The big toys go on the bottom, the medium ones go in the middle, and the small ones go on top.

But what if you also wanted to sort them based on their color? That would make finding them even easier, right?

This is where an implicit KD-tree comes in.

An implicit KD-tree is like having multiple toy boxes stacked on top of each other, with each box sorting the toys based on a different characteristic.

So, in our example, we would have one box for size and one box for color. The big toys would go in the bottom box, and then each box above it would break down the toys further by size.

The next box up would sort toys by color, and then each box above it would break down the toys further by color.

When you want to find a toy, you just have to look in the right box. If you're looking for a big red toy, you can just open the bottom two boxes and quickly find what you're looking for.

An implicit KD-tree works the same way. It's a way of organizing data into a multi-dimensional tree structure, with each level of the tree breaking down the data further by specific characteristics.

When you want to search for a specific piece of data, you just have to follow the tree down to the point where the data matches your search criteria. It's a really efficient way of finding data in large datasets!