Self-similarity: Difference between revisions
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[[File:Standard self-similarity.png|thumb|300px|Standard (trivial) self-similarity<ref>Mandelbrot, Benoit B. (1982). ''The Fractal Geometry of Nature'', p.44. {{ISBN|978-0716711865}}.</ref>]] | [[File:Standard self-similarity.png|thumb|300px|Standard (trivial) self-similarity<ref>Mandelbrot, Benoit B. (1982). ''The Fractal Geometry of Nature'', p.44. {{ISBN|978-0716711865}}.</ref>]] | ||
In [[mathematics]], a '''self-similar''' object is exactly or approximately [[similarity (geometry)|similar]] to a part of itself (i.e., the whole has the same shape as one or more of the parts). Many objects in the real world, such as [[coastline]]s, are statistically self-similar: parts of them show the same statistical properties at many scales.<ref name="Mandelbrot_Science_1967">{{cite journal | title=How long is the coast of Britain? Statistical self-similarity and fractional dimension | journal=[[Science (journal)|Science]] | date=5 May 1967 | author=Mandelbrot, Benoit B. | pages=636–638 | volume=156 | number=3775 | doi=10.1126/science.156.3775.636 | series=New Series | pmid=17837158 | bibcode=1967Sci...156..636M | s2cid=15662830 | url=http://ena.lp.edu.ua:8080/handle/ntb/52473 | access-date=12 November 2020 | archive-date=19 October 2021 | archive-url=https://web.archive.org/web/20211019193011/http://ena.lp.edu.ua:8080/handle/ntb/52473 | url-status=dead }} [http://users.math.yale.edu/~bbm3/web_pdfs/howLongIsTheCoastOfBritain.pdf PDF]</ref> Self-similarity is a typical property of [[fractal]]s. [[Scale invariance]] is an exact form of self-similarity where at any magnification there is a smaller piece of the object that is [[Similarity (geometry)|similar]] to the whole. For instance, a side of the [[Koch snowflake]] is both [[symmetrical]] and scale-invariant; it can be continually magnified 3x without changing shape | In [[mathematics]], a '''self-similar''' object is exactly or approximately [[similarity (geometry)|similar]] to a part of itself (i.e., the whole has the same shape as one or more of the parts). Many objects in the real world, such as [[coastline]]s, are statistically self-similar: parts of them show the same statistical properties at many scales.<ref name="Mandelbrot_Science_1967">{{cite journal | title=How long is the coast of Britain? Statistical self-similarity and fractional dimension | journal=[[Science (journal)|Science]] | date=5 May 1967 | author=Mandelbrot, Benoit B. | pages=636–638 | volume=156 | number=3775 | doi=10.1126/science.156.3775.636 | series=New Series | pmid=17837158 | bibcode=1967Sci...156..636M | s2cid=15662830 | url=http://ena.lp.edu.ua:8080/handle/ntb/52473 | access-date=12 November 2020 | archive-date=19 October 2021 | archive-url=https://web.archive.org/web/20211019193011/http://ena.lp.edu.ua:8080/handle/ntb/52473 | url-status=dead }} [http://users.math.yale.edu/~bbm3/web_pdfs/howLongIsTheCoastOfBritain.pdf PDF]</ref> Self-similarity is a typical property of [[fractal]]s. [[Scale invariance]] is an exact form of self-similarity where at any magnification there is a smaller piece of the object that is [[Similarity (geometry)|similar]] to the whole. For instance, a side of the [[Koch snowflake]] is both [[symmetrical]] and scale-invariant; it can be continually magnified 3x without changing shape. | ||
[[Heinz-Otto Peitgen|Peitgen]] ''et al.'' explain the concept as such: | [[Heinz-Otto Peitgen|Peitgen]] ''et al.'' explain the concept as such: | ||
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[[Image:Self-affine set.png|thumb|right|A self-affine fractal with [[Hausdorff dimension]] = 1.8272]] | [[Image:Self-affine set.png|thumb|right|A self-affine fractal with [[Hausdorff dimension]] = 1.8272]] | ||
In [[mathematics]], '''self-affinity''' is a feature of a [[fractal]] whose pieces are [[scaling (geometry)|scaled]] by different amounts in the ''x'' and ''y'' directions. This means that to appreciate the self-similarity of these fractal objects, they have to be rescaled using an [[anisotropic]] [[affine transformation]]. | In [[mathematics]], '''self-affinity''' is a feature of a [[fractal]] whose pieces are [[scaling (geometry)|scaled]] by different amounts in the ''x'' and ''y'' directions. This means that to appreciate the self-similarity of these fractal objects, they have to be rescaled using an [[anisotropic]] [[affine transformation]].{{cn|date=October 2025}} | ||
==Definition== | ==Definition== | ||
A [[Compact space|compact]] [[topological space]] ''X'' is self-similar if there exists a [[finite set]] ''S'' indexing a set of non-[[surjective]] [[homeomorphism]]s <math>\{ f_s : s\in S \}</math> for which | {{unreferenced-section|date=October 2025}} | ||
A [[Compact space|compact]] [[topological space]] ''X'' is self-similar if there exists a [[finite set]] ''S'' indexing a set of non-[[surjective]] [[homeomorphism]]s <math>\{ f_s : s\in S \}</math> for which <ref>{{cite web |url=https://home.mathematik.uni-freiburg.de/prunescu/SelfSim.pdf |title=Self-similar carpets over finite fields |website=Mathematical Institute Albert Ludwigs University of Freiburg |access-date=2025-11-19}}</ref> | |||
:<math>X=\bigcup_{s\in S} f_s(X)</math> | :<math>X=\bigcup_{s\in S} f_s(X)</math> | ||
If <math>X\subset Y</math>, we call ''X'' self-similar if it is the only [[Non-empty set|non-empty]] [[subset]] of ''Y'' such that the equation above holds for <math>\{ f_s : s\in S \} </math>. We call | If <math>X\subset Y</math>, we call ''X'' self-similar if it is the only [[Non-empty set|non-empty]] [[subset]] of ''Y'' such that the equation above holds for <math>\{ f_s : s\in S \} </math>. We call. <ref>{{cite web |url=https://home.mathematik.uni-freiburg.de/prunescu/SelfSim.pdf |title=Self-similar carpets over finite fields |website=Mathematical Institute Albert Ludwigs University of Freiburg |access-date=2025-11-19}}</ref> | ||
:<math>\mathfrak{L}=(X,S,\{ f_s : s\in S \} )</math> | :<math>\mathfrak{L}=(X,S,\{ f_s : s\in S \} )</math> | ||
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[[Image:Feigenbaumzoom.gif|left|thumb|201px|Self-similarity in the [[Mandelbrot set]] shown by zooming in on the Feigenbaum point at (−1.401155189..., 0)]] | [[Image:Feigenbaumzoom.gif|left|thumb|201px|Self-similarity in the [[Mandelbrot set]] shown by zooming in on the Feigenbaum point at (−1.401155189..., 0)]] | ||
[[Image:Fractal fern explained.png|thumb|right|300px|An image of the [[Barnsley fern]] which exhibits [[affine transformation|affine]] self-similarity]] | [[Image:Fractal fern explained.png|thumb|right|300px|An image of the [[Barnsley fern]] which exhibits [[affine transformation|affine]] self-similarity]] | ||
The [[Cantor discontinuum]] is self-similar since any of its closed subsets is a continuous image of the discontinuum.<ref>[[Kazimierz Kuratowski]] (1972) Leo F. Boron, translator, ''Introduction to Set Theory and Topology'', second edition, ch XVI, § 8 The Cantor Discontinuum, page 210 to 15, [[Pergamon Press]]</ref> | |||
The [[Mandelbrot set]] is also self-similar around [[Misiurewicz point]]s. | The [[Mandelbrot set]] is also self-similar around [[Misiurewicz point]]s. | ||
Self-similarity has important consequences for the design of computer networks, as typical network traffic has self-similar properties. For example, in [[teletraffic engineering]], [[packet switched]] data traffic patterns seem to be statistically self-similar.<ref>{{cite journal|last1=Leland|first1=W.E.|last2=Taqqu|first2=M.S.|last3=Willinger|first3=W.|last4=Wilson|first4=D.V.|display-authors=2|title=On the self-similar nature of Ethernet traffic (extended version)|journal=IEEE/ACM Transactions on Networking|date=January 1995|volume=2|issue=1|pages=1–15|doi=10.1109/90.282603|s2cid=6011907|url=http://ccr.sigcomm.org/archive/1995/jan95/ccr-9501-leland.pdf}}</ref> This property means that simple models using a [[Poisson distribution]] are inaccurate, and networks designed without taking self-similarity into account are likely to function in unexpected ways. | Self-similarity has important consequences for the design of computer networks, as typical network traffic has self-similar properties. For example, in [[teletraffic engineering]], [[packet switched]] data traffic patterns seem to be statistically self-similar.<ref>{{cite journal|last1=Leland|first1=W.E.|last2=Taqqu|first2=M.S.|last3=Willinger|first3=W.|last4=Wilson|first4=D.V.|display-authors=2|title=On the self-similar nature of Ethernet traffic (extended version)|journal=IEEE/ACM Transactions on Networking|date=January 1995|volume=2|issue=1|pages=1–15|doi=10.1109/90.282603|s2cid=6011907|url=http://ccr.sigcomm.org/archive/1995/jan95/ccr-9501-leland.pdf}}</ref> This property means that simple models using a [[Poisson distribution]] are inaccurate, and networks designed without taking self-similarity into account are likely to function in unexpected ways.{{cn|date=October 2025}} | ||
Similarly, [[stock market]] movements are described as displaying [[self-affinity]], i.e. they appear self-similar when transformed via an appropriate [[affine transformation]] for the level of detail being shown.<ref>{{cite magazine | url=https://www.scientificamerican.com/article/multifractals-explain-wall-street/ | title=How Fractals Can Explain What's Wrong with Wall Street | author=Benoit Mandelbrot | magazine=Scientific American| | Similarly, [[stock market]] movements are described as displaying [[self-affinity]], i.e. they appear self-similar when transformed via an appropriate [[affine transformation]] for the level of detail being shown.<ref>{{cite magazine | url=https://www.scientificamerican.com/article/multifractals-explain-wall-street/ | title=How Fractals Can Explain What's Wrong with Wall Street | author=Benoit Mandelbrot | magazine=Scientific American| | ||
date=February 1999| author-link=Benoit Mandelbrot}}</ref> [[Andrew Lo]] describes stock market log return self-similarity in [[econometrics]].<ref>Campbell, Lo and MacKinlay (1991) "[[Econometrics]] of Financial Markets ", Princeton University Press! {{ISBN|978-0691043012}}</ref> | date=February 1999| author-link=Benoit Mandelbrot}}</ref> [[Andrew Lo]] describes stock market log return self-similarity in [[econometrics]].<ref>Campbell, Lo and MacKinlay (1991) "[[Econometrics]] of Financial Markets ", Princeton University Press! {{ISBN|978-0691043012}}</ref> | ||
[[Finite subdivision rules]] are a powerful technique for building self-similar sets, including the [[Cantor set]] and the [[Sierpinski triangle]]. | [[Finite subdivision rules]] are a powerful technique for building self-similar sets, including the [[Cantor set]] and the [[Sierpinski triangle]].{{cn|date=October 2025}} | ||
Some [[space filling curves]], such as the [[Peano curve]] and [[Moore curve]], also feature properties of self-similarity.<ref>{{Cite web |last=Salazar |first=Munera |last2=Eduardo |first2=Luis |date=July 1, 2016 |title=Self-Similarity of Space Filling Curves |url=https://repository.icesi.edu.co/items/5f9b8cea-4787-7785-e053-2cc003c84dc5 |url-status=live |archive-url=https://web.archive.org/web/20250313193207/https://repository.icesi.edu.co/items/5f9b8cea-4787-7785-e053-2cc003c84dc5 |archive-date=March 13, 2025 |access-date=March 13, 2025 |website=Universidad ICESI}}</ref> | Some [[space filling curves]], such as the [[Peano curve]] and [[Moore curve]], also feature properties of self-similarity.<ref>{{Cite web |last=Salazar |first=Munera |last2=Eduardo |first2=Luis |date=July 1, 2016 |title=Self-Similarity of Space Filling Curves |url=https://repository.icesi.edu.co/items/5f9b8cea-4787-7785-e053-2cc003c84dc5 |url-status=live |archive-url=https://web.archive.org/web/20250313193207/https://repository.icesi.edu.co/items/5f9b8cea-4787-7785-e053-2cc003c84dc5 |archive-date=March 13, 2025 |access-date=March 13, 2025 |website=Universidad ICESI}}</ref> | ||
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=== In cybernetics === | === In cybernetics === | ||
The [[viable system model]] of [[Stafford Beer]] is an organizational model with an affine self-similar hierarchy, where a given viable system is one element of the System One of a viable system one recursive level higher up, and for whom the elements of its System One are viable systems one recursive level lower down. | The [[viable system model]] of [[Stafford Beer]] is an organizational model with an affine self-similar hierarchy, where a given viable system is one element of the System One of a viable system one recursive level higher up, and for whom the elements of its System One are viable systems one recursive level lower down.{{cn|date=October 2025}} | ||
=== In nature === | === In nature === | ||
[[File:Flickr - cyclonebill - Romanesco.jpg|thumb|right|200px|Close-up of a [[Romanesco broccoli]]]] | [[File:Flickr - cyclonebill - Romanesco.jpg|thumb|right|200px|Close-up of a [[Romanesco broccoli]]]] | ||
{{further|Patterns in nature}} | {{further|Patterns in nature}} | ||
Self-similarity can be found in nature, as well. Plants, such as [[Romanesco broccoli]], exhibit strong self-similarity. | Self-similarity can be found in nature, as well. Plants, such as [[Romanesco broccoli]], exhibit strong self-similarity.{{cn|date=October 2025}} | ||
=== In music === | === In music === | ||
* Strict [[canon (music)|canons]] display various types and amounts of self-similarity, as do sections of [[fugue (music)|fugues]]. | * Strict [[canon (music)|canons]] display various types and amounts of self-similarity, as do sections of [[fugue (music)|fugues]].{{cn|date=October 2025}} | ||
* A [[Shepard tone]] is self-similar in the frequency or wavelength domains. | * A [[Shepard tone]] is self-similar in the frequency or wavelength domains.{{cn|date=October 2025}} | ||
* The | * The Danish composer [[Per Nørgård]] made use of a self-similar [[integer sequence]] named the [[infinity series]] in much of his music.{{cn|date=October 2025}} | ||
* In the research field of [[music information retrieval]], self-similarity commonly refers to the fact that music often consists of parts that are repeated in time.<ref>{{cite book |last1=Foote |first1=Jonathan |title=Proceedings of the seventh ACM international conference on Multimedia (Part 1) |chapter=Visualizing music and audio using self-similarity |date=30 October 1999 |pages=77–80 |doi=10.1145/319463.319472 |url=http://musicweb.ucsd.edu/~sdubnov/CATbox/Reader/p77-foote.pdf |url-status=live |archive-url=https://web.archive.org/web/20170809032554/http://musicweb.ucsd.edu/~sdubnov/CATbox/Reader/p77-foote.pdf |archive-date=9 August 2017|isbn=978-1581131512 |citeseerx=10.1.1.223.194 |s2cid=3329298 }}</ref> In other words, music is self-similar under temporal translation, rather than (or in addition to) under scaling.<ref>{{cite book |last1=Pareyon |first1=Gabriel |title=On Musical Self-Similarity: Intersemiosis as Synecdoche and Analogy |date=April 2011 |publisher=International Semiotics Institute at Imatra; Semiotic Society of Finland |isbn=978-952-5431-32-2 |page=240 |url=https://tuhat.helsinki.fi/portal/files/15216101/Pareyon_Dissertation.pdf |access-date=30 July 2018 |archive-url=https://web.archive.org/web/20170208034152/https://tuhat.helsinki.fi/portal/files/15216101/Pareyon_Dissertation.pdf |archive-date=8 February 2017}} (Also see [https://books.google.com/books?id=xQIynayPqMQC&pg=PA240&lpg=PA240&focus=viewport&vq=%221/f+noise+substantially+as+a+temporal+phenomenon%22 Google Books])</ref> | * In the research field of [[music information retrieval]], self-similarity commonly refers to the fact that music often consists of parts that are repeated in time.<ref>{{cite book |last1=Foote |first1=Jonathan |title=Proceedings of the seventh ACM international conference on Multimedia (Part 1) |chapter=Visualizing music and audio using self-similarity |date=30 October 1999 |pages=77–80 |doi=10.1145/319463.319472 |url=http://musicweb.ucsd.edu/~sdubnov/CATbox/Reader/p77-foote.pdf |url-status=live |archive-url=https://web.archive.org/web/20170809032554/http://musicweb.ucsd.edu/~sdubnov/CATbox/Reader/p77-foote.pdf |archive-date=9 August 2017|isbn=978-1581131512 |citeseerx=10.1.1.223.194 |s2cid=3329298 }}</ref> In other words, music is self-similar under temporal translation, rather than (or in addition to) under scaling.<ref>{{cite book |last1=Pareyon |first1=Gabriel |title=On Musical Self-Similarity: Intersemiosis as Synecdoche and Analogy |date=April 2011 |publisher=International Semiotics Institute at Imatra; Semiotic Society of Finland |isbn=978-952-5431-32-2 |page=240 |url=https://tuhat.helsinki.fi/portal/files/15216101/Pareyon_Dissertation.pdf |access-date=30 July 2018 |archive-url=https://web.archive.org/web/20170208034152/https://tuhat.helsinki.fi/portal/files/15216101/Pareyon_Dissertation.pdf |archive-date=8 February 2017}} (Also see [https://books.google.com/books?id=xQIynayPqMQC&pg=PA240&lpg=PA240&focus=viewport&vq=%221/f+noise+substantially+as+a+temporal+phenomenon%22 Google Books])</ref> | ||
Latest revision as of 15:42, 19 November 2025
Template:Short description Template:Use dmy dates
In mathematics, a self-similar object is exactly or approximately similar to a part of itself (i.e., the whole has the same shape as one or more of the parts). Many objects in the real world, such as coastlines, are statistically self-similar: parts of them show the same statistical properties at many scales.[2] Self-similarity is a typical property of fractals. Scale invariance is an exact form of self-similarity where at any magnification there is a smaller piece of the object that is similar to the whole. For instance, a side of the Koch snowflake is both symmetrical and scale-invariant; it can be continually magnified 3x without changing shape.
Peitgen et al. explain the concept as such:
Template:QuoteSince mathematically, a fractal may show self-similarity under arbitrary magnification, it is impossible to recreate this physically. Peitgen et al. suggest studying self-similarity using approximations:Template:Quote
This vocabulary was introduced by Benoit Mandelbrot in 1964.[3]
Self-affinity
In mathematics, self-affinity is a feature of a fractal whose pieces are scaled by different amounts in the x and y directions. This means that to appreciate the self-similarity of these fractal objects, they have to be rescaled using an anisotropic affine transformation.Script error: No such module "Unsubst".
Definition
Script error: No such module "Unsubst". A compact topological space X is self-similar if there exists a finite set S indexing a set of non-surjective homeomorphisms for which [4]
If , we call X self-similar if it is the only non-empty subset of Y such that the equation above holds for . We call. [5]
a self-similar structure. The homeomorphisms may be iterated, resulting in an iterated function system. The composition of functions creates the algebraic structure of a monoid. When the set S has only two elements, the monoid is known as the dyadic monoid. The dyadic monoid can be visualized as an infinite binary tree; more generally, if the set S has p elements, then the monoid may be represented as a p-adic tree.
The automorphisms of the dyadic monoid is the modular group; the automorphisms can be pictured as hyperbolic rotations of the binary tree.
A more general notion than self-similarity is self-affinity.
Examples
The Cantor discontinuum is self-similar since any of its closed subsets is a continuous image of the discontinuum.[6]
The Mandelbrot set is also self-similar around Misiurewicz points.
Self-similarity has important consequences for the design of computer networks, as typical network traffic has self-similar properties. For example, in teletraffic engineering, packet switched data traffic patterns seem to be statistically self-similar.[7] This property means that simple models using a Poisson distribution are inaccurate, and networks designed without taking self-similarity into account are likely to function in unexpected ways.Script error: No such module "Unsubst".
Similarly, stock market movements are described as displaying self-affinity, i.e. they appear self-similar when transformed via an appropriate affine transformation for the level of detail being shown.[8] Andrew Lo describes stock market log return self-similarity in econometrics.[9]
Finite subdivision rules are a powerful technique for building self-similar sets, including the Cantor set and the Sierpinski triangle.Script error: No such module "Unsubst".
Some space filling curves, such as the Peano curve and Moore curve, also feature properties of self-similarity.[10]
In cybernetics
The viable system model of Stafford Beer is an organizational model with an affine self-similar hierarchy, where a given viable system is one element of the System One of a viable system one recursive level higher up, and for whom the elements of its System One are viable systems one recursive level lower down.Script error: No such module "Unsubst".
In nature
Script error: No such module "labelled list hatnote". Self-similarity can be found in nature, as well. Plants, such as Romanesco broccoli, exhibit strong self-similarity.Script error: No such module "Unsubst".
In music
- Strict canons display various types and amounts of self-similarity, as do sections of fugues.Script error: No such module "Unsubst".
- A Shepard tone is self-similar in the frequency or wavelength domains.Script error: No such module "Unsubst".
- The Danish composer Per Nørgård made use of a self-similar integer sequence named the infinity series in much of his music.Script error: No such module "Unsubst".
- In the research field of music information retrieval, self-similarity commonly refers to the fact that music often consists of parts that are repeated in time.[11] In other words, music is self-similar under temporal translation, rather than (or in addition to) under scaling.[12]
See also
References
External links
- "Copperplate Chevrons" — a self-similar fractal zoom movie
- "Self-Similarity" — New articles about Self-Similarity. Waltz Algorithm
Self-affinity
- Script error: No such module "Citation/CS1".
- Script error: No such module "Citation/CS1".
- Script error: No such module "citation/CS1".
- ↑ Mandelbrot, Benoit B. (1982). The Fractal Geometry of Nature, p.44. Template:ISBN.
- ↑ Script error: No such module "Citation/CS1". PDF
- ↑ Comment j'ai découvert les fractales, Interview de Benoit Mandelbrot, La Recherche https://www.larecherche.fr/math%C3%A9matiques-histoire-des-sciences/%C2%AB-comment-jai-d%C3%A9couvert-les-fractales-%C2%BB
- ↑ Script error: No such module "citation/CS1".
- ↑ Script error: No such module "citation/CS1".
- ↑ Kazimierz Kuratowski (1972) Leo F. Boron, translator, Introduction to Set Theory and Topology, second edition, ch XVI, § 8 The Cantor Discontinuum, page 210 to 15, Pergamon Press
- ↑ Script error: No such module "Citation/CS1".
- ↑ Template:Cite magazine
- ↑ Campbell, Lo and MacKinlay (1991) "Econometrics of Financial Markets ", Princeton University Press! Template:ISBN
- ↑ Script error: No such module "citation/CS1".
- ↑ Script error: No such module "citation/CS1".
- ↑ Script error: No such module "citation/CS1". (Also see Google Books)