en>Monkbot: /* WINNOWER and SP-STAR */Task 5: Fix CS1 deprecated coauthor parameter errors

2014-07-21T23:27:57Z

WINNOWER and SP-STAR: Task 5: Fix CS1 deprecated coauthor parameter errors

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	~~In [[statistics]]~~ and ~~[[data mining]], '''affinity propagation''' (AP) is a [[Cluster analysis\|clustering algorithm]] based on the concept of "message passing" between data points.~~<~~ref name="science"~~>{{cite journal \|author1=Brendan J. Frey \|author2=Delbert Dueck \|title=Clustering by passing messages between data points \|journal=[[Science (journal)\|Science]] \|volume=315 \|year=2007 \|pages=972–976}}<~~/ref~~>		Infrared Thermometers and Their Application in Automotive Industry<br><br>Many people on earth have several dreams and wishes that belongs to them. Of these, the most desired wishes of several people is to have night vision. Not many people understand that their dream to see the objects during night has been made reality through the thermal infrared goggles. Some of the lots of benefits provided by thermal infrared goggles are listed below:<br><br>Speed cameras aren't new. Several types were developed in the 1950-60's. Currently, Europe, Canada, and Australia rely on them a great deal. The newer digital computer-controlled models are rapidly becoming popular with enforcement systems because they permit fast accurate money-saving processing. In fact, many red-light cameras can also gauge the pace of an vehicle while it passes by way of a green light.<br><br>Near infrared spectroscopy offers some advantages over other kinds of spectroscopy. Since the signal to noise ratio of NIR technology is great, background readings, which may modify the accuracy of an scan, are below those of sample test results. Lower noise levels help doctors and scientists get clearer, more accurate comes from near infrared reflectance tests.<br><br>Charbroil Red grill<br>The Charbroil Red grill is really a special version of the grill only sold at Home Depot.  It works on the different burner technology compared to the Quantum.  Instead of a metal screen, you can find solid metal troughs under the grate.  The biggest benefit of this design is that it is less difficult to completely clean.<br><br>The most common claim of health advantage of infrared saunas is: your body which stimulated by radiant heat can release pollutants, chemicals, and toxins. These materials include nickel, mercury, copper, arsenic, lead, sulfuric acid, sodium, and nicotine. Infrared heat saunas always used for that reason purpose - detoxification. All of the nasty toxins will be cleansed through sweating.<br><br>When you liked this information in addition to you wish to receive more information regarding http://blbuh.Ru/infrared_Heat_708490 generously stop by our own web site.
	~~Unlike clustering algorithms such as [[K-means clustering\|''k''-means]] or [[K-medoids\|''k''-medoids]]~~, ~~AP does not require~~ the ~~number~~ of ~~clusters~~ to ~~be determined or estimated before running~~ the ~~algorithm~~. ~~Like ''k''-medoids, AP finds "exemplars", members~~ of the ~~input set that~~ are ~~representative of clusters.~~<~~ref name="science"/~~>

	~~==Algorithm==~~
	~~Let~~ '~~'x''₁ through ''xₙ'' be a set of data points, with no assumptions made about their internal structure, and let ''s'' be a function that quantifies~~ the ~~similarity between any two points, that is ''s''(''xᵢ'', ''xⱼ'') > '~~'s~~''(''xᵢ'', ''xₖ'') [[if and only if\|iff]] ''xⱼ'' is more similar to ''xᵢ'' than ''xₖ''~~.

	~~The algorithm proceeds by alternating two message passing steps~~, ~~to update two matrices:<ref name="science"/>~~

	* The "responsibility" matrix ''R'' has values ''r''(''i'', ~~''k'') that quantify how suited ''xₖ'' is to serve as the exemplar for ''xᵢ''~~, ~~relative to other candidate exemplars for ''xᵢ''~~.
	* The "availability" matrix ''A'' contains values ''a''(''i'', ''k'') represents how "appropriate" it would be for ''xᵢ'' to pick ''xₖ'' as its exemplar, taking into account other points' preference for ''xₖ'' as an exemplar.

	~~Both matrices~~ are ~~initialized to all zeroes, and can be viewed as [[log~~-~~probability]] tables~~. ~~The algorithm then performs the following updates iteratively:~~

	* First, ~~responsibility updates are sent around: <math>r(i,k) \leftarrow s(i,k)~~ - ~~\max_{k' \neq k} \left\{~~ a~~(i,k') + s(i,k') \right\}~~<~~/math~~>
	* Then, availability is updated per
	::<~~math~~>~~a(i~~,~~k) \leftarrow \min \left( 0~~, ~~r(k~~,~~k) + \sum_{i' \not\in \{i~~,~~k\}} \max(0, r(i',k)) \right)</math> for~~ <~~math~~>~~i \neq k~~<~~/math~~> ~~and~~
	::<~~math~~>a(k,~~k) \leftarrow \sum_{i' \neq k} \max(0, r(i',k))</math>~~.

	~~==Applications==~~
	The ~~inventors~~ of ~~affinity propagation showed~~ it is ~~better for certain computer vision and computational biology tasks, e~~.~~g. clustering of pictures of human faces and identifying regulated transcripts, than ''k''-means,~~<~~ref name="science"/~~> ~~even when ''k''-means was allowed many random restarts and initialized using [[Principal components analysis\|PCA]].~~<~~ref~~>{{cite conference \|author1=Delbert Dueck \|author2=Brendan J. Frey \|title=Non-metric affinity propagation for unsupervised image categorization \|conference=Int'l Conf. on Computer Vision \|year=2007}}</ref>
	~~Affinity propagation has been successfully used to solve problems in a wide range~~ of ~~areas~~, ~~including vision~~, ~~genomics~~, ~~genetic networks~~, ~~biology~~, ~~circuit design~~, ~~nuclear physics~~, ~~biochemistry~~, ~~nanotechnology~~, ~~geology, anthropology, economics~~ and ~~financial modelling~~. In many applications it works better than other techniques, but it is not guaranteed to work better than other methods. For example, a study comparing AP and [[Markov clustering]] on [[protein interaction graph]] partitioning found Markov clustering to work better for that ~~problem~~.<~~ref~~>{{cite journal \|author1=James Vlasblom \|author2=Shoshana Wodak \|title=Markov clustering versus affinity propagation for the partitioning of protein interaction graphs \|journal=BMC Bioinformatics \|volume=10 \|number=1 \|year=2009 \|pages=99 \|url=http://~~www~~.~~biomedcentral~~.~~com/1471-2105/10/99/}}</ref>~~

	~~==References==~~
	~~{{reflist\|2}}~~

	~~{{compu-sci-stub}}~~

	~~[[Category:Data clustering algorithms]]~~

en>Bgwhite: WP:CHECKWIKI error fix #94. Stray ref tag. Do general fixes and cleanup if needed. - using AWB (9890)

2014-01-29T08:58:02Z

WP:CHECKWIKI error fix #94. Stray ref tag. Do general fixes and cleanup if needed. - using AWB (9890)

New page

In [[statistics]] and [[data mining]], '''affinity propagation''' (AP) is a [[Cluster analysis|clustering algorithm]] based on the concept of "message passing" between data points.<ref name="science">{{cite journal |author1=Brendan J. Frey |author2=Delbert Dueck |title=Clustering by passing messages between data points |journal=[[Science (journal)|Science]] |volume=315 |year=2007 |pages=972–976}}</ref>
Unlike clustering algorithms such as [[K-means clustering|''k''-means]] or [[K-medoids|''k''-medoids]], AP does not require the number of clusters to be determined or estimated before running the algorithm. Like ''k''-medoids, AP finds "exemplars", members of the input set that are representative of clusters.<ref name="science"/>

==Algorithm==
Let ''x''₁ through ''xₙ'' be a set of data points, with no assumptions made about their internal structure, and let ''s'' be a function that quantifies the similarity between any two points, that is ''s''(''xᵢ'', ''xⱼ'') > ''s''(''xᵢ'', ''xₖ'') [[if and only if|iff]] ''xⱼ'' is more similar to ''xᵢ'' than ''xₖ''.

The algorithm proceeds by alternating two message passing steps, to update two matrices:<ref name="science"/>

* The "responsibility" matrix ''R'' has values ''r''(''i'', ''k'') that quantify how suited ''xₖ'' is to serve as the exemplar for ''xᵢ'', relative to other candidate exemplars for ''xᵢ''.
* The "availability" matrix ''A'' contains values ''a''(''i'', ''k'') represents how "appropriate" it would be for ''xᵢ'' to pick ''xₖ'' as its exemplar, taking into account other points' preference for ''xₖ'' as an exemplar.

Both matrices are initialized to all zeroes, and can be viewed as [[log-probability]] tables. The algorithm then performs the following updates iteratively:

* First, responsibility updates are sent around: <math>r(i,k) \leftarrow s(i,k) - \max_{k' \neq k} \left\{ a(i,k') + s(i,k') \right\}</math>
* Then, availability is updated per
::<math>a(i,k) \leftarrow \min \left( 0, r(k,k) + \sum_{i' \not\in \{i,k\}} \max(0, r(i',k)) \right)</math> for <math>i \neq k</math> and
::<math>a(k,k) \leftarrow \sum_{i' \neq k} \max(0, r(i',k))</math>.

==Applications==
The inventors of affinity propagation showed it is better for certain computer vision and computational biology tasks, e.g. clustering of pictures of human faces and identifying regulated transcripts, than ''k''-means,<ref name="science"/> even when ''k''-means was allowed many random restarts and initialized using [[Principal components analysis|PCA]].<ref>{{cite conference |author1=Delbert Dueck |author2=Brendan J. Frey |title=Non-metric affinity propagation for unsupervised image categorization |conference=Int'l Conf. on Computer Vision |year=2007}}</ref>
Affinity propagation has been successfully used to solve problems in a wide range of areas, including vision, genomics, genetic networks, biology, circuit design, nuclear physics, biochemistry, nanotechnology, geology, anthropology, economics and financial modelling. In many applications it works better than other techniques, but it is not guaranteed to work better than other methods. For example, a study comparing AP and [[Markov clustering]] on [[protein interaction graph]] partitioning found Markov clustering to work better for that problem.<ref>{{cite journal |author1=James Vlasblom |author2=Shoshana Wodak |title=Markov clustering versus affinity propagation for the partitioning of protein interaction graphs |journal=BMC Bioinformatics |volume=10 |number=1 |year=2009 |pages=99 |url=http://www.biomedcentral.com/1471-2105/10/99/}}</ref>

==References==
{{reflist|2}}

{{compu-sci-stub}}

[[Category:Data clustering algorithms]]

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en>Monkbot: /* WINNOWER and SP-STAR */Task 5: Fix CS1 deprecated coauthor parameter errors

en>Bgwhite: WP:CHECKWIKI error fix #94. Stray ref tag. Do general fixes and cleanup if needed. - using AWB (9890)