Fast approximate hierarchical clustering using similarity heuristics

Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity pose...
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Autor*in:	Kull, Meelis [verfasserIn] Vilo, Jaak

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Hierarchical Cluster Pairwise Distance Agglomerative Hierarchical Cluster Gene Ontology Term Hierarchical Cluster Algorithm

Anmerkung:	© Kull and Vilo; licensee BioMed Central Ltd. 2008

Übergeordnetes Werk:	Enthalten in: BioData Mining - London : BioMed Central, 2008, 1(2008), 1 vom: 22. Sept.
Übergeordnetes Werk:	volume:1 ; year:2008 ; number:1 ; day:22 ; month:09

Links:	Volltext

DOI / URN:	10.1186/1756-0381-1-9

Katalog-ID:	SPR029585406

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520			\|a Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust
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allfields	10.1186/1756-0381-1-9 doi (DE-627)SPR029585406 (SPR)1756-0381-1-9-e DE-627 ger DE-627 rakwb eng Kull, Meelis verfasserin aut Fast approximate hierarchical clustering using similarity heuristics 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kull and Vilo; licensee BioMed Central Ltd. 2008 Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust Hierarchical Cluster (dpeaa)DE-He213 Pairwise Distance (dpeaa)DE-He213 Agglomerative Hierarchical Cluster (dpeaa)DE-He213 Gene Ontology Term (dpeaa)DE-He213 Hierarchical Cluster Algorithm (dpeaa)DE-He213 Vilo, Jaak aut Enthalten in BioData Mining London : BioMed Central, 2008 1(2008), 1 vom: 22. Sept. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:1 year:2008 number:1 day:22 month:09 https://dx.doi.org/10.1186/1756-0381-1-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2008 1 22 09
spelling	10.1186/1756-0381-1-9 doi (DE-627)SPR029585406 (SPR)1756-0381-1-9-e DE-627 ger DE-627 rakwb eng Kull, Meelis verfasserin aut Fast approximate hierarchical clustering using similarity heuristics 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kull and Vilo; licensee BioMed Central Ltd. 2008 Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust Hierarchical Cluster (dpeaa)DE-He213 Pairwise Distance (dpeaa)DE-He213 Agglomerative Hierarchical Cluster (dpeaa)DE-He213 Gene Ontology Term (dpeaa)DE-He213 Hierarchical Cluster Algorithm (dpeaa)DE-He213 Vilo, Jaak aut Enthalten in BioData Mining London : BioMed Central, 2008 1(2008), 1 vom: 22. Sept. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:1 year:2008 number:1 day:22 month:09 https://dx.doi.org/10.1186/1756-0381-1-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2008 1 22 09
allfields_unstemmed	10.1186/1756-0381-1-9 doi (DE-627)SPR029585406 (SPR)1756-0381-1-9-e DE-627 ger DE-627 rakwb eng Kull, Meelis verfasserin aut Fast approximate hierarchical clustering using similarity heuristics 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kull and Vilo; licensee BioMed Central Ltd. 2008 Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust Hierarchical Cluster (dpeaa)DE-He213 Pairwise Distance (dpeaa)DE-He213 Agglomerative Hierarchical Cluster (dpeaa)DE-He213 Gene Ontology Term (dpeaa)DE-He213 Hierarchical Cluster Algorithm (dpeaa)DE-He213 Vilo, Jaak aut Enthalten in BioData Mining London : BioMed Central, 2008 1(2008), 1 vom: 22. Sept. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:1 year:2008 number:1 day:22 month:09 https://dx.doi.org/10.1186/1756-0381-1-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2008 1 22 09
allfieldsGer	10.1186/1756-0381-1-9 doi (DE-627)SPR029585406 (SPR)1756-0381-1-9-e DE-627 ger DE-627 rakwb eng Kull, Meelis verfasserin aut Fast approximate hierarchical clustering using similarity heuristics 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kull and Vilo; licensee BioMed Central Ltd. 2008 Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust Hierarchical Cluster (dpeaa)DE-He213 Pairwise Distance (dpeaa)DE-He213 Agglomerative Hierarchical Cluster (dpeaa)DE-He213 Gene Ontology Term (dpeaa)DE-He213 Hierarchical Cluster Algorithm (dpeaa)DE-He213 Vilo, Jaak aut Enthalten in BioData Mining London : BioMed Central, 2008 1(2008), 1 vom: 22. Sept. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:1 year:2008 number:1 day:22 month:09 https://dx.doi.org/10.1186/1756-0381-1-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2008 1 22 09
allfieldsSound	10.1186/1756-0381-1-9 doi (DE-627)SPR029585406 (SPR)1756-0381-1-9-e DE-627 ger DE-627 rakwb eng Kull, Meelis verfasserin aut Fast approximate hierarchical clustering using similarity heuristics 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kull and Vilo; licensee BioMed Central Ltd. 2008 Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust Hierarchical Cluster (dpeaa)DE-He213 Pairwise Distance (dpeaa)DE-He213 Agglomerative Hierarchical Cluster (dpeaa)DE-He213 Gene Ontology Term (dpeaa)DE-He213 Hierarchical Cluster Algorithm (dpeaa)DE-He213 Vilo, Jaak aut Enthalten in BioData Mining London : BioMed Central, 2008 1(2008), 1 vom: 22. Sept. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:1 year:2008 number:1 day:22 month:09 https://dx.doi.org/10.1186/1756-0381-1-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2008 1 22 09
language	English
source	Enthalten in BioData Mining 1(2008), 1 vom: 22. Sept. volume:1 year:2008 number:1 day:22 month:09
sourceStr	Enthalten in BioData Mining 1(2008), 1 vom: 22. Sept. volume:1 year:2008 number:1 day:22 month:09
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Hierarchical Cluster Pairwise Distance Agglomerative Hierarchical Cluster Gene Ontology Term Hierarchical Cluster Algorithm
isfreeaccess_bool	true
container_title	BioData Mining
authorswithroles_txt_mv	Kull, Meelis @@aut@@ Vilo, Jaak @@aut@@
publishDateDaySort_date	2008-09-22T00:00:00Z
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Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. 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author	Kull, Meelis
spellingShingle	Kull, Meelis misc Hierarchical Cluster misc Pairwise Distance misc Agglomerative Hierarchical Cluster misc Gene Ontology Term misc Hierarchical Cluster Algorithm Fast approximate hierarchical clustering using similarity heuristics
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topic_title	Fast approximate hierarchical clustering using similarity heuristics Hierarchical Cluster (dpeaa)DE-He213 Pairwise Distance (dpeaa)DE-He213 Agglomerative Hierarchical Cluster (dpeaa)DE-He213 Gene Ontology Term (dpeaa)DE-He213 Hierarchical Cluster Algorithm (dpeaa)DE-He213
topic	misc Hierarchical Cluster misc Pairwise Distance misc Agglomerative Hierarchical Cluster misc Gene Ontology Term misc Hierarchical Cluster Algorithm
topic_unstemmed	misc Hierarchical Cluster misc Pairwise Distance misc Agglomerative Hierarchical Cluster misc Gene Ontology Term misc Hierarchical Cluster Algorithm
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title	Fast approximate hierarchical clustering using similarity heuristics
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title_full	Fast approximate hierarchical clustering using similarity heuristics
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title_sort	fast approximate hierarchical clustering using similarity heuristics
title_auth	Fast approximate hierarchical clustering using similarity heuristics
abstract	Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust © Kull and Vilo; licensee BioMed Central Ltd. 2008
abstractGer	Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust © Kull and Vilo; licensee BioMed Central Ltd. 2008
abstract_unstemmed	Background Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust © Kull and Vilo; licensee BioMed Central Ltd. 2008
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title_short	Fast approximate hierarchical clustering using similarity heuristics
url	https://dx.doi.org/10.1186/1756-0381-1-9
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Fast approximate hierarchical clustering using similarity heuristics

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