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## Capacity Dimension

In order to find the capacity dimension of a set, we assume that the number of elements covering a data set is inversely proportional to eD, where e is the scale of covering elements and D is a constant. For example, we have a line segment and we try to cover the segment with squares of a certain size, and find that we need three squares. If we then tried to see how many squares of half the original size were required to cover the segment, it could be expected to have six squares covering the segment, which is twice the number of squares needed when the squares were at their original size. Thus, the number of squares required to cover the segment is inversely proportional to the size of the squares. The covering of any smooth, continuous curve works the same way, provided that the size of the squares is small enough so that the curve is approximated well by straight line segments at that scale.

Thus, for one-dimensional objects, we see that

where e is the side of the square, N(e) is the number of squares of that size required to cover the set, and kis some constant. Now suppose that we are covering a scrap of paper with little squares. In this case, if we halve the size of the squares, it would take four of the smaller squares to cover what one of the larger squares would cover, and so we would expect N(e) to increase by a factor of four when e is halved. This is consistent with an equation of the form,

It seems reasonable to say that for more arbitrary sets,

where D is the dimension of the set. In other words, we can hope to measure how much of two-dimensional space some subset of it comes near by examining how efficiently the set can be covered by cells of different size.

In order to find D from , we can solve the formula for D, by taking the limit as . This is the capacity method of estimating D. If we further assume that the set is scaled so that it fits into a square with side 1, then we get N(1)=k=1. This yields the formula,

Next: Correlation Dimension Up: Defining Fractals and Fractal Previous: Defining Fractals and Fractal
Sandip Thanki
1999-07-29