kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering

Abdulmohsen Almalawi; Adil Fahad; Zahir Tari; Muhammad Aamir Cheema; Ibrahim Khalil

doi:10.1109/ICDE.2016.7498431

kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering

Abdulmohsen Almalawi, Adil Fahad, Zahir Tari, Muhammad Aamir Cheema, Ibrahim Khalil

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

2 Citations (Scopus)

Abstract

In this paper, a novel k-NN approach based on Various-Widths Clustering, named kNNVWC, is proposed to efficiently find k-NNs for a query object from a given data set. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. Experimental results demonstrate that kNNVWC performs well compared to state-ofart of k-NN search algorithms.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE International Conference on Data Engineering (ICDE 2016)
Editors	Mei Hsu, Alfons Kemper, Timos Sellis
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1572-1573
Number of pages	2
ISBN (Electronic)	9781509020195
DOIs	https://doi.org/10.1109/ICDE.2016.7498431
Publication status	Published - 22 Jun 2016
Event	IEEE International Conference on Data Engineering 2016 - Aalto University School of Business, Helsinki, Finland Duration: 16 May 2016 → 20 May 2016 Conference number: 32nd http://icde2016.fi/ https://ieeexplore.ieee.org/xpl/conhome/7491900/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Data Engineering 2016
Abbreviated title	ICDE 2016
Country/Territory	Finland
City	Helsinki
Period	16/05/16 → 20/05/16
Other	The annual ICDE conference addresses research issues in designing, building, managing, and evaluating advanced data systems and applications. It is a leading forum for researchers, practitioners, developers, and users to explore cutting-edge ideas and to exchange techniques, tools, and experiences.
Internet address	http://icde2016.fi/ https://ieeexplore.ieee.org/xpl/conhome/7491900/proceeding (Proceedings)

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10.1109/ICDE.2016.7498431

Cite this

Almalawi, A., Fahad, A., Tari, Z., Cheema, M. A., & Khalil, I. (2016). kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering. In M. Hsu, A. Kemper, & T. Sellis (Eds.), Proceedings of the 2016 IEEE International Conference on Data Engineering (ICDE 2016) (pp. 1572-1573). Article 7498431 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDE.2016.7498431

@inproceedings{af482a5348a74d72b260a8d778f3b73b,

title = "kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering",

abstract = "In this paper, a novel k-NN approach based on Various-Widths Clustering, named kNNVWC, is proposed to efficiently find k-NNs for a query object from a given data set. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. Experimental results demonstrate that kNNVWC performs well compared to state-ofart of k-NN search algorithms.",

author = "Abdulmohsen Almalawi and Adil Fahad and Zahir Tari and Cheema, \{Muhammad Aamir\} and Ibrahim Khalil",

year = "2016",

month = jun,

day = "22",

doi = "10.1109/ICDE.2016.7498431",

language = "English",

pages = "1572--1573",

editor = "Mei Hsu and Alfons Kemper and Timos Sellis",

booktitle = "Proceedings of the 2016 IEEE International Conference on Data Engineering (ICDE 2016)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Conference on Data Engineering 2016, ICDE 2016 ; Conference date: 16-05-2016 Through 20-05-2016",

url = "http://icde2016.fi/, https://ieeexplore.ieee.org/xpl/conhome/7491900/proceeding",

}

Almalawi, A, Fahad, A, Tari, Z, Cheema, MA & Khalil, I 2016, kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering. in M Hsu, A Kemper & T Sellis (eds), Proceedings of the 2016 IEEE International Conference on Data Engineering (ICDE 2016)., 7498431, IEEE, Institute of Electrical and Electronics Engineers, pp. 1572-1573, IEEE International Conference on Data Engineering 2016, Helsinki, Finland, 16/05/16. https://doi.org/10.1109/ICDE.2016.7498431

kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering. / Almalawi, Abdulmohsen; Fahad, Adil; Tari, Zahir et al.
Proceedings of the 2016 IEEE International Conference on Data Engineering (ICDE 2016). ed. / Mei Hsu; Alfons Kemper; Timos Sellis. IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 1572-1573 7498431.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

TY - GEN

T1 - kNNVWC

T2 - IEEE International Conference on Data Engineering 2016

AU - Almalawi, Abdulmohsen

AU - Fahad, Adil

AU - Tari, Zahir

AU - Cheema, Muhammad Aamir

AU - Khalil, Ibrahim

N1 - Conference code: 32nd

PY - 2016/6/22

Y1 - 2016/6/22

N2 - In this paper, a novel k-NN approach based on Various-Widths Clustering, named kNNVWC, is proposed to efficiently find k-NNs for a query object from a given data set. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. Experimental results demonstrate that kNNVWC performs well compared to state-ofart of k-NN search algorithms.

AB - In this paper, a novel k-NN approach based on Various-Widths Clustering, named kNNVWC, is proposed to efficiently find k-NNs for a query object from a given data set. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. Experimental results demonstrate that kNNVWC performs well compared to state-ofart of k-NN search algorithms.

UR - https://www.scopus.com/pages/publications/84980322288

U2 - 10.1109/ICDE.2016.7498431

DO - 10.1109/ICDE.2016.7498431

M3 - Conference Paper

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BT - Proceedings of the 2016 IEEE International Conference on Data Engineering (ICDE 2016)

A2 - Hsu, Mei

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PB - IEEE, Institute of Electrical and Electronics Engineers

Y2 - 16 May 2016 through 20 May 2016

ER -

Almalawi A, Fahad A, Tari Z, Cheema MA, Khalil I. kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering. In Hsu M, Kemper A, Sellis T, editors, Proceedings of the 2016 IEEE International Conference on Data Engineering (ICDE 2016). IEEE, Institute of Electrical and Electronics Engineers. 2016. p. 1572-1573. 7498431 doi: 10.1109/ICDE.2016.7498431

kNNVWC: An efficient k-nearest neighbours approach based on various-widths clustering

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kNNVWC: An efficient k-nearest neighbors approach based on various-widths clustering

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