Unsupervised ensemble minority clustering

Cluster a alysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against...

Full description

Bibliographic Details
Authors: González Pellicer, Edgar, Turmo Borras, Jorge|||0000-0002-7521-1115
Format: report
Publication Date:2012
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/15664
Online Access:https://hdl.handle.net/2117/15664
Access Level:Open access
Keyword:Clustering
Clústers
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
id ES_f7fd27a0ef9d424bbea82df659b3ab8a
oai_identifier_str oai:upcommons.upc.edu:2117/15664
network_acronym_str ES
network_name_str España
repository_id_str
spelling Unsupervised ensemble minority clusteringGonzález Pellicer, EdgarTurmo Borras, Jorge|||0000-0002-7521-1115ClusteringClústersÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge naturalCluster a alysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise. The approaches proposed so far for minority clustering are supervised: they require the number and distribution of the foreground and background clusters. In supervised learning and all-in clustering, combination methods have been successfully applied to obtain distribution-free learners, even from the output of weak individual algorithms. In this report, we present a novel ensemble minority clustering algorithm, Ewocs, suitable for weak clustering combination, and provide a theoretical proof of its properties under a loose set of constraints. The validity of the assumptions used in the proof is empirically assessed using a collection of synthetic datasets.20122012-03-0120122012-03-26reporthttp://purl.org/coar/resource_type/c_93fcAOhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/2117/15664reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/156642026-05-27T15:37:01Z
dc.title.none.fl_str_mv Unsupervised ensemble minority clustering
title Unsupervised ensemble minority clustering
spellingShingle Unsupervised ensemble minority clustering
González Pellicer, Edgar
Clustering
Clústers
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
title_short Unsupervised ensemble minority clustering
title_full Unsupervised ensemble minority clustering
title_fullStr Unsupervised ensemble minority clustering
title_full_unstemmed Unsupervised ensemble minority clustering
title_sort Unsupervised ensemble minority clustering
dc.creator.none.fl_str_mv González Pellicer, Edgar
Turmo Borras, Jorge|||0000-0002-7521-1115
author González Pellicer, Edgar
author_facet González Pellicer, Edgar
Turmo Borras, Jorge|||0000-0002-7521-1115
author_role author
author2 Turmo Borras, Jorge|||0000-0002-7521-1115
author2_role author
dc.subject.none.fl_str_mv Clustering
Clústers
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
topic Clustering
Clústers
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
description Cluster a alysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise. The approaches proposed so far for minority clustering are supervised: they require the number and distribution of the foreground and background clusters. In supervised learning and all-in clustering, combination methods have been successfully applied to obtain distribution-free learners, even from the output of weak individual algorithms. In this report, we present a novel ensemble minority clustering algorithm, Ewocs, suitable for weak clustering combination, and provide a theoretical proof of its properties under a loose set of constraints. The validity of the assumptions used in the proof is empirically assessed using a collection of synthetic datasets.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-03-01
2012
2012-03-26
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
AO
http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/15664
url https://hdl.handle.net/2117/15664
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869424959817777152
score 15,300719

Similar Items