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=> Apollonian Gasket
=> Barnsley's Fern
=> Barnsley's Tree
=> Batrachion
=> Blancmange Function
=> Box Fractal
=> Brown Function
=> Cactus Fractal
=> Cantor Dust
=> Cantor Function
=> Cantor Set
=> Cantor Square Fractal
=> Capacity Dimension
=> Carotid-Kundalini Fractal
=> Cesàro Fractal
=> Chaos Game
=> Circles-and-Squares Fractal
=> Coastline Paradox
=> Correlation Exponent
=> Count
=> Cross-Stitch Curve
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=> Hénon Map
=> Hilbert Curve
=> Householder's Method
=> Ice Fractal
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=> Koch Antisnowflake
=> Koch Snowflake
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=> Lévy Tapestry
=> Lindenmayer System
=> Mandelbrot Set
=> Mandelbrot Set Lemniscate
=> Mandelbrot Tree
=> Menger Sponge
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=> Mira Fractal
=> Newton's Method
=> Peano Curve
=> Peano-Gosper Curve
=> Pentaflake
=> Plane-Filling Function
=> Pythagoras Tree
=> Randelbrot Set
=> Rep-Tile
=> Reverend Back's Abbey Floor
=> San Marco Fractal
=> Sea Horse Valley
=> Siegel Disk Fractal
=> Sierpiński Arrowhead Curve
=> Sierpiński Carpet
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=> Sierpiński Sieve
=> Star Fractal
=> Strange Attractor
=> Tetrix
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Ziyaretçi defteri
 

Capacity Dimension

A dimension also called the fractal dimension, Hausdorff dimension, and Hausdorff-Besicovitch dimension in which nonintegral values are permitted. Objects whose capacity dimension is different from their Lebesgue covering dimension are called fractals. The capacity dimension of a compact metric space X is a real number d_(capacity) such that if n(epsilon) denotes the minimum number of open sets of diameter less than or equal to epsilon, then n(epsilon) is proportional to epsilon^(-D) as epsilon->0. Explicitly,

 d_(capacity)=-lim_(epsilon->0^+)(lnN)/(lnepsilon)

(if the limit exists), where N is the number of elements forming a finite cover of the relevant metric space and epsilon is a bound on the diameter of the sets involved (informally, epsilon is the size of each element used to cover the set, which is taken to approach 0). If each element of a fractal is equally likely to be visited, then d_(capacity)=d_(information), where d_(information) is the information dimension.

The capacity dimension satisfies

 d_(correlation)<=d_(information)<=d_(capacity)

where d_(correlation) is the correlation dimension (correcting the typo in Baker and Gollub 1996).


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