A data partitioning approach for hierarchical clustering

Seok Ho Yoon; Suk Soon Song; Sang Chul Lee; Kyo Sung Jeong; Sang Wook Kim; Sooyong Kang; Yong Suk Choi; Jaehyuk Cha; Minsoo Ryu; Byung Soo Jeong

doi:10.1145/2448556.2448628

A data partitioning approach for hierarchical clustering

Seok Ho Yoon^*, Suk Soon Song, Sang Chul Lee, Kyo Sung Jeong, Sang Wook Kim, Sooyong Kang, Yong Suk Choi, Jaehyuk Cha, Minsoo Ryu, Byung Soo Jeong

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

4 Citations (Scopus)

Abstract

In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierarchical clustering algorithm. The proposed method splits a given dataset into every possible number of clusters by using existing algorithms that do allow arbitrary-sized sub-clusters in partitioning. After that, it evaluates the quality of every set of initial sub-clusters by using our measurement function, and decides the optimal set of initial sub-clusters such that they show the highest value of measurement. Finally, it merges these optimal initial sub-clusters repeatedly and produces the final clustering result. We perform extensive experiments, and the results show that the proposed approach is insensitive to parameters and also produces a set of final clusters whose quality is better than the previous one.

Original language	English
Title of host publication	ICUIMC 2013
Subtitle of host publication	Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Place of Publication	New York
Pages	1-4
Number of pages	4
DOIs	https://doi.org/10.1145/2448556.2448628
Publication status	Published - 2013
Externally published	Yes
Event	7th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2013 - Kota Kinabalu, Malaysia Duration: 17 Jan 2013 → 19 Jan 2013

Other

Other	7th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2013
Country/Territory	Malaysia
City	Kota Kinabalu
Period	17/01/13 → 19/01/13

Keywords

Data partitioning
Hierarchical clustering
Parameter-insensitive

Access to Document

10.1145/2448556.2448628

Cite this

@inproceedings{66457691316e4d5bae4357949cdf1b2b,

title = "A data partitioning approach for hierarchical clustering",

abstract = "In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierarchical clustering algorithm. The proposed method splits a given dataset into every possible number of clusters by using existing algorithms that do allow arbitrary-sized sub-clusters in partitioning. After that, it evaluates the quality of every set of initial sub-clusters by using our measurement function, and decides the optimal set of initial sub-clusters such that they show the highest value of measurement. Finally, it merges these optimal initial sub-clusters repeatedly and produces the final clustering result. We perform extensive experiments, and the results show that the proposed approach is insensitive to parameters and also produces a set of final clusters whose quality is better than the previous one.",

keywords = "Data partitioning, Hierarchical clustering, Parameter-insensitive",

author = "Yoon, {Seok Ho} and Song, {Suk Soon} and Lee, {Sang Chul} and Jeong, {Kyo Sung} and Kim, {Sang Wook} and Sooyong Kang and Choi, {Yong Suk} and Jaehyuk Cha and Minsoo Ryu and Jeong, {Byung Soo}",

year = "2013",

doi = "10.1145/2448556.2448628",

language = "English",

isbn = "9781450319584",

pages = "1--4",

booktitle = "ICUIMC 2013",

note = "7th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2013 ; Conference date: 17-01-2013 Through 19-01-2013",

}

Yoon, SH, Song, SS, Lee, SC, Jeong, KS, Kim, SW, Kang, S, Choi, YS, Cha, J, Ryu, M & Jeong, BS 2013, A data partitioning approach for hierarchical clustering. in ICUIMC 2013: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication., 72, New York, pp. 1-4, 7th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2013, Kota Kinabalu, Malaysia, 17/01/13. https://doi.org/10.1145/2448556.2448628

A data partitioning approach for hierarchical clustering. / Yoon, Seok Ho; Song, Suk Soon; Lee, Sang Chul et al.
ICUIMC 2013: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication. New York, 2013. p. 1-4 72.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - A data partitioning approach for hierarchical clustering

AU - Yoon, Seok Ho

AU - Song, Suk Soon

AU - Lee, Sang Chul

AU - Jeong, Kyo Sung

AU - Kim, Sang Wook

AU - Kang, Sooyong

AU - Choi, Yong Suk

AU - Cha, Jaehyuk

AU - Ryu, Minsoo

AU - Jeong, Byung Soo

PY - 2013

Y1 - 2013

N2 - In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierarchical clustering algorithm. The proposed method splits a given dataset into every possible number of clusters by using existing algorithms that do allow arbitrary-sized sub-clusters in partitioning. After that, it evaluates the quality of every set of initial sub-clusters by using our measurement function, and decides the optimal set of initial sub-clusters such that they show the highest value of measurement. Finally, it merges these optimal initial sub-clusters repeatedly and produces the final clustering result. We perform extensive experiments, and the results show that the proposed approach is insensitive to parameters and also produces a set of final clusters whose quality is better than the previous one.

AB - In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierarchical clustering algorithm. The proposed method splits a given dataset into every possible number of clusters by using existing algorithms that do allow arbitrary-sized sub-clusters in partitioning. After that, it evaluates the quality of every set of initial sub-clusters by using our measurement function, and decides the optimal set of initial sub-clusters such that they show the highest value of measurement. Finally, it merges these optimal initial sub-clusters repeatedly and produces the final clustering result. We perform extensive experiments, and the results show that the proposed approach is insensitive to parameters and also produces a set of final clusters whose quality is better than the previous one.

KW - Data partitioning

KW - Hierarchical clustering

KW - Parameter-insensitive

UR - http://www.scopus.com/inward/record.url?scp=84875842002&partnerID=8YFLogxK

U2 - 10.1145/2448556.2448628

DO - 10.1145/2448556.2448628

M3 - Conference proceeding contribution

AN - SCOPUS:84875842002

SN - 9781450319584

SP - 1

EP - 4

BT - ICUIMC 2013

CY - New York

T2 - 7th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2013

Y2 - 17 January 2013 through 19 January 2013

ER -

A data partitioning approach for hierarchical clustering

Abstract

Other

Keywords

Access to Document

Other files and links

Fingerprint

Cite this