IEEE Trans Pattern Anal Mach Intell - A Link-Based Approach to the Cluster Ensemble Problem.

Tópicos

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{ problem(2511) optim(1539) algorithm(950) }
{ perform(999) metric(946) measur(919) }
{ howev(809) still(633) remain(590) }
{ network(2748) neural(1063) input(814) }
{ method(1557) propos(1049) approach(1037) }
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{ imag(2830) propos(1344) filter(1198) }
{ studi(1410) differ(1259) use(1210) }
{ use(1733) differ(960) four(931) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
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{ inform(2794) health(2639) internet(1427) }
{ care(1570) inform(1187) nurs(1089) }
{ featur(1941) imag(1645) propos(1176) }
{ system(1050) medic(1026) inform(1018) }
{ compound(1573) activ(1297) structur(1058) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
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{ chang(1828) time(1643) increas(1301) }
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Resumo

Cluster ensembles have recently emerged as a powerful alternative to standard cluster analysis, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. From the early work, these techniques held great promise; however, most of them generate the final solution based on incomplete information of a cluster ensemble. The underlying ensemble-information matrix reflects only cluster-data point relations while those amongst clusters are generally overlooked. This paper presents a new link-based approach to improve the conventional matrix. It achieves this using the similarity between clusters that are estimated from a link network model of the ensemble. In particular, three new link-based algorithms are proposed for the underlying similarity assessment. The final clustering result is generated from the refined matrix using two different consensus functions of feature-based and graph-based partitioning. This approach is the first to address and explicitly employ the relationship between input partitions, which has not been emphasized by the recent studies of matrix refinement. The effectiveness of the link-based approach is empirically demonstrated over ten datasets (synthetic and real) and three benchmark evaluation measures. The results suggest the new approach is able to efficiently extract information embedded in the input clusterings, and consistently demonstrate higher clustering quality in comparison to several state-of-the-art techniques.

Resumo Limpo

cluster ensembl recent emerg power altern standard cluster analysi aggreg sever input data cluster generat singl output cluster improv robust stabil earli work techniqu held great promis howev generat final solut base incomplet inform cluster ensembl under ensembleinform matrix reflect clusterdata point relat amongst cluster general overlook paper present new linkbas approach improv convent matrix achiev use similar cluster estim link network model ensembl particular three new linkbas algorithm propos under similar assess final cluster result generat refin matrix use two differ consensus function featurebas graphbas partit approach first address explicit employ relationship input partit emphas recent studi matrix refin effect linkbas approach empir demonstr ten dataset synthet real three benchmark evalu measur result suggest new approach abl effici extract inform embed input cluster consist demonstr higher cluster qualiti comparison sever stateoftheart techniqu

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