Nearest Neighbors by Neighborhood Counting

Research output: Contribution to journalArticle

100 Citations (Scopus)
LanguageEnglish
Pages942-953
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume28
Issue number6
DOIs
Publication statusPublished - 1 Jun 2006

Cite this

@article{5c117a1d7af145c4851341133322943b,
title = "Nearest Neighbors by Neighborhood Counting",
author = "H Wang",
note = "Other Details ------------------------------------ There exist numerous similarity measures, but there is no generic measure that applies to different types of data. This paper presents a conceptually uniform, generic approach to measuring similarity: count common neighbourhoods. This approach has resulted in novel similarity measures for multivariate data, sequences and trees. Evaluation shows that they outperform a range of state-of-the-art measures. This work formed the basis of an EPSRC proposal on structural information retrieval. Although the proposal was not funded, it was viewed as highly innovative and ambitious. A revised proposal has been prepared and will be submitted shortly.",
year = "2006",
month = "6",
day = "1",
doi = "10.1109/TPAMI.2006.126",
language = "English",
volume = "28",
pages = "942--953",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
issn = "0162-8828",
number = "6",

}

Nearest Neighbors by Neighborhood Counting. / Wang, H.

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 6, 01.06.2006, p. 942-953.

Research output: Contribution to journalArticle

TY - JOUR

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AU - Wang, H

N1 - Other Details ------------------------------------ There exist numerous similarity measures, but there is no generic measure that applies to different types of data. This paper presents a conceptually uniform, generic approach to measuring similarity: count common neighbourhoods. This approach has resulted in novel similarity measures for multivariate data, sequences and trees. Evaluation shows that they outperform a range of state-of-the-art measures. This work formed the basis of an EPSRC proposal on structural information retrieval. Although the proposal was not funded, it was viewed as highly innovative and ambitious. A revised proposal has been prepared and will be submitted shortly.

PY - 2006/6/1

Y1 - 2006/6/1

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M3 - Article

VL - 28

SP - 942

EP - 953

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

T2 - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 6

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