Robust, generalized, quick and efficient agglomerative clustering

Wallace, M; Kollias, S

dc.contributor.author	Wallace, M	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:42:57Z
dc.date.available	2014-03-01T02:42:57Z
dc.date.issued	2004	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31150
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-8444241518&partnerID=40&md5=3a58639fbe315e40b3d1dd1244afa0cd	en
dc.relation.uri	http://www.image.ece.ntua.gr/papers/259.pdf	en
dc.relation.uri	http://www.informatik.uni-trier.de/~ley/db/conf/iceis/iceis2004-2.html#WallaceK04	en
dc.subject	Agglomerative clustering	en
dc.subject	Dimensionality curse	en
dc.subject	Feature selection	en
dc.subject	Machine learning	en
dc.subject	Soft computing	en
dc.subject	Unsupervised techniques	en
dc.subject.other	Agglomeration	en
dc.subject.other	Algorithms	en
dc.subject.other	Data reduction	en
dc.subject.other	Error analysis	en
dc.subject.other	Information analysis	en
dc.subject.other	Large scale systems	en
dc.subject.other	Learning systems	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Robustness (control systems)	en
dc.subject.other	Agglomerative clustering	en
dc.subject.other	Dimensionality curse	en
dc.subject.other	Feature selection	en
dc.subject.other	Soft computing	en
dc.subject.other	Unsupervised techniques	en
dc.subject.other	Neural networks	en
dc.title	Robust, generalized, quick and efficient agglomerative clustering	en
heal.type	conferenceItem	en
heal.publicationDate	2004	en
heal.abstract	Hierarchical approaches, which are dominated by the generic agglomerative clustering algorithm, are suitable for cases in which the count of distinct clusters in the data is not known a priori; this is not a rare case in real data. On the other hand, important problems are related to their application, such as susceptibility to errors in the initial steps that propagate all the way to the final output and high complexity. Finally, similarly to all other clustering techniques, their efficiency decreases as the dimensionality of their input increases. In this paper we propose a robust, generalized, quick and efficient extension to the generic agglomerative clustering process. Robust refers to the proposed approach's ability to overcome the classic algorithm's susceptibility to errors in the initial steps, generalized to its ability to simultaneously consider multiple distance metrics, quick to its suitability for application to larger datasets via the application of the computationally expensive components to only a subset of the available data samples and efficient to its ability to produce results that are comparable to those of trained classifiers, largely outperforming the generic agglomerative process.	en
heal.journalName	ICEIS 2004 - Proceedings of the Sixth International Conference on Enterprise Information Systems	en
dc.identifier.spage	409	en
dc.identifier.epage	416	en