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dc.contributor.authorChu, S-C
dc.contributor.authorRoddick, John Francis
dc.contributor.authorChen, Tsong-Yi
dc.contributor.authorPan, Jeng-Shyang
dc.date.accessioned2011-12-06T22:47:30Z
dc.date.available2011-12-06T22:47:30Z
dc.date.issued2002
dc.identifier.citationChu, S.-C., Roddick, J., Chen, T.-Y. and Pan, J.-S. 2002. Efficient search approaches for K-medoids-based algorithms. 2002 IEEE Conference on Computers, Communications, Control and Power Engineering, vol. 1, 712a - 715a.en
dc.identifier.isbn780374908-
dc.identifier.urihttp://hdl.handle.net/2328/25786
dc.description.abstractIn this paper, the concept of previous medoid index is introduced. The utilization of memory for efficient medoid search is also presented. We propose a hybrid search approach for the problem of nearest neighbor search. The hybrid search approach combines the previous medoid index, the utilization of memory, the criterion of triangular inequality elimination and the partial distance search. The proposed hybrid search approach is applied to the k-medoids-based algorithms. Experimental results based on Gauss-Markov source, curve data set and elliptic clusters demonstrate that the proposed algorithm applied to the CLARANS algorithm may reduce the number of distance calculations from 88.4% to 95.2% with the same average distance per object compared with CLARANS. The proposed hybrid search approach can also be applied to nearest neighbor searching and the other clustering algorithms.en
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Computer Society (IEEE Publishing)en
dc.subjectComputational complexityen
dc.subjectPattern clusteringen
dc.subjectSearch problemsen
dc.titleEfficient search approaches for K-medoids-based algorithmsen
dc.typeArticleen
dc.rights.licenseIn Copyright
local.contributor.authorOrcidLookupRoddick, John Francis: https://orcid.org/0000-0001-7024-0796en_US


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