An extensive experimental study on the cluster-based reference set
reduction for speeding-up the k-NN classifier

dc.contributor.author	Ουγιάρογλου, Στέφανος	el
dc.contributor.author	Ευαγγελίδης, Γεώργιος	el
dc.contributor.author	Δέρβος, Δημήτριος Α.	el
dc.contributor.author	Ougiaroglou, Stefanos	en
dc.contributor.author	Evangelidis, Georgios	en
dc.contributor.author	Dervos, Dimitris A.	en
dc.date.accessioned	2015-04-25T08:06:10Z
dc.date.available	2015-04-25T08:06:10Z
dc.date.issued	2015-04-25
dc.identifier.uri	http://hdl.handle.net/11400/8904
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://history.icininfo.net/2011/	el
dc.source	http://history.icininfo.net/2011/FileStore/procs_INFO_2011.pdf	el
dc.subject	Data reduction
dc.subject	Computer networks--Scalability
dc.subject	K-NN classification
dc.subject	K-NN κατάταξη
dc.subject	Clustering
dc.subject	Ομαδοποίηση
dc.subject	Μείωση δεδομένων
dc.subject	Επεκτασιμότητα
dc.title	An extensive experimental study on the cluster-based reference set reduction for speeding-up the k-NN classifier	en
heal.type	conferenceItem
heal.classification	Computer science
heal.classification	Computer programming
heal.classification	Πληροφορική
heal.classification	Προγραμματισμός
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://skos.um.es/unescothes/C00749
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Προγραμματισμός
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85035860
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004878
heal.contributorName	Γιαννακόπουλος, Γεώργιος Α. (συντ.)	el
heal.contributorName	Σακκάς, Δαμιανός Π. (συντ.)	el
heal.language	en
heal.access	free
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Διοίκησης και Οικονομίας. Τμήμα Βιβλιοθηκονομίας και Συστημάτων Πληροφόρησης	el
heal.publicationDate	2011-09
heal.bibliographicCitation	Ougiaroglou, S., Evangelidis, G. and Dervos, D.A. (2011). An extensive experimental study on the cluster-based reference set reduction for speeding-up the k-NN classifier. International Conference on Integrated Information (IC-ININFO 2011), Kos Island, Greece, 29 September - 3 Octomber 2011. pp. 12-15. Available from: http://history.icininfo.net/2011/FileStore/procs_INFO_2011.pdf	en
heal.abstract	The k-Nearest Neighbor (k-NN) classification algorithm is one of the most widely-used lazy classifiers because of its simplicity and ease of implementation. It is considered to be an effective classifier and has many applications. However, its major drawback is that when sequential search is used to find the neighbors, it involves high computational cost. Speeding-up k-NN search is still an active research field. Hwang and Cho have recently proposed an adaptive cluster-based method for fast Nearest Neighbor searching. The effectiveness of this method is based on the adjustment of three parameters. However, the authors evaluated their method by setting specific parameter values and using only one dataset. In this paper, an extensive experimental study of this method is presented. The results, which are based on five real life datasets, illustrate that if the parameters of the method are carefully defined, one can achieve even better classification performance.	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference on Integrated Information (IC-ININFO 2011)	el
heal.conferenceItemType	full paper