Basically, the idea is that we want to build trees that start at the root and are very broad in the descriptions of the object. A good parallel to this is how we classify vehicles. We start with the make, then the model, then the year, then the color, and so on. Also another good example is classifying living things: Vertebrates vs. Invertebrates, mammals, and so on.


• Use of TV-tree
  • Objects classified in attribute space can be bounded by their similarities
    This bounded space is then split up again and again, thus creating children that are more specifically described
    
    • The shape of the bounding region can be hyper-rectangles, cubes, spheres, and so on
  • TV-N trees are ones with height N
  • When selecting nodes to insert, priority is given to the leaves whose bounding regions are "closer" to the new object inserted
  • Picking the initial bounding regions is done with initial data and typically selecting the two farthest points. The distance between the two is calculated and then split into 2 to form two different regions.
• Other methods X tree, R* tree