Improved CFDP algorithms based on shared nearest neighbors and transitive closure

Li Ni; Wenjian Luo; Chenyang Bu; Yamin Hu

doi:10.1007/978-3-319-67274-8_8

Improved CFDP algorithms based on shared nearest neighbors and transitive closure

Li Ni
, Wenjian Luo^*
, Chenyang Bu
, Yamin Hu

^*Corresponding author for this work

University of Science and Technology of China

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

4 Scopus citations

Abstract

A recently proposed clustering algorithm named Clustering by fast search and Find of Density Peaks (CFDP) can automatically identify the cluster centers without an iterative process. The key step in CFDP is searching for the nearest neighbor with higher density for each point. However, the CFDP algorithm may not be effective for cases in which there exist density fluctuations within a cluster or between two nearby clusters. In this study, two improved algorithms named CFDP-ED-TSNN1 and CFDP-ED-TSNN2 are presented, which adopt different ways to utilize the dissimilarity. Here, the dissimilarity is based on shared nearest neighbors and transitive closure. The experimental results on both several artificial datasets and a real-world dataset show that the improved algorithms are competitive.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Revised Selected Papers
Editors	Yang-Sae Moon, U Kang, Jeffrey Xu Yu, Ee-Peng Lim
Publisher	Springer Verlag
Pages	79-93
Number of pages	15
ISBN (Print)	9783319672731
DOIs	https://doi.org/10.1007/978-3-319-67274-8_8
State	Published - 2017
Externally published	Yes
Event	21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 held in conjuction with the Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2017, Workshop on Biologically Inspired Data-Mining Techniques, BDM 2017, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2017 and Workshop on Data Mining in Business Process Management, DM-BPM 2017 - Jeju, Korea, Republic of Duration: 23 May 2017 → 23 May 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10526 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 held in conjuction with the Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2017, Workshop on Biologically Inspired Data-Mining Techniques, BDM 2017, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2017 and Workshop on Data Mining in Business Process Management, DM-BPM 2017
Country/Territory	Korea, Republic of
City	Jeju
Period	23/05/17 → 23/05/17

Keywords

Clustering
Shared nearest neighbors
Transitive closure

Access to Document

10.1007/978-3-319-67274-8_8

Cite this

Ni, L., Luo, W., Bu, C., & Hu, Y. (2017). Improved CFDP algorithms based on shared nearest neighbors and transitive closure. In Y.-S. Moon, U. Kang, J. X. Yu, & E.-P. Lim (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Revised Selected Papers (pp. 79-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10526 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-67274-8_8

Ni, Li ; Luo, Wenjian ; Bu, Chenyang et al. / Improved CFDP algorithms based on shared nearest neighbors and transitive closure. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Revised Selected Papers. editor / Yang-Sae Moon ; U Kang ; Jeffrey Xu Yu ; Ee-Peng Lim. Springer Verlag, 2017. pp. 79-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d0d85ae51db54689accb083204f5eb90,

title = "Improved CFDP algorithms based on shared nearest neighbors and transitive closure",

abstract = "A recently proposed clustering algorithm named Clustering by fast search and Find of Density Peaks (CFDP) can automatically identify the cluster centers without an iterative process. The key step in CFDP is searching for the nearest neighbor with higher density for each point. However, the CFDP algorithm may not be effective for cases in which there exist density fluctuations within a cluster or between two nearby clusters. In this study, two improved algorithms named CFDP-ED-TSNN1 and CFDP-ED-TSNN2 are presented, which adopt different ways to utilize the dissimilarity. Here, the dissimilarity is based on shared nearest neighbors and transitive closure. The experimental results on both several artificial datasets and a real-world dataset show that the improved algorithms are competitive.",

keywords = "Clustering, Shared nearest neighbors, Transitive closure",

author = "Li Ni and Wenjian Luo and Chenyang Bu and Yamin Hu",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 held in conjuction with the Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2017, Workshop on Biologically Inspired Data-Mining Techniques, BDM 2017, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2017 and Workshop on Data Mining in Business Process Management, DM-BPM 2017 ; Conference date: 23-05-2017 Through 23-05-2017",

year = "2017",

doi = "10.1007/978-3-319-67274-8\_8",

language = "英语",

isbn = "9783319672731",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "Yang-Sae Moon and U Kang and Yu, \{Jeffrey Xu\} and Ee-Peng Lim",

booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Revised Selected Papers",

address = "德国",

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Ni, L, Luo, W, Bu, C & Hu, Y 2017, Improved CFDP algorithms based on shared nearest neighbors and transitive closure. in Y-S Moon, U Kang, JX Yu & E-P Lim (eds), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10526 LNAI, Springer Verlag, pp. 79-93, 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 held in conjuction with the Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2017, Workshop on Biologically Inspired Data-Mining Techniques, BDM 2017, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2017 and Workshop on Data Mining in Business Process Management, DM-BPM 2017, Jeju, Korea, Republic of, 23/05/17. https://doi.org/10.1007/978-3-319-67274-8_8

Improved CFDP algorithms based on shared nearest neighbors and transitive closure. / Ni, Li; Luo, Wenjian; Bu, Chenyang et al.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Revised Selected Papers. ed. / Yang-Sae Moon; U Kang; Jeffrey Xu Yu; Ee-Peng Lim. Springer Verlag, 2017. p. 79-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10526 LNAI).

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

TY - GEN

T1 - Improved CFDP algorithms based on shared nearest neighbors and transitive closure

AU - Ni, Li

AU - Luo, Wenjian

AU - Bu, Chenyang

AU - Hu, Yamin

PY - 2017

Y1 - 2017

N2 - A recently proposed clustering algorithm named Clustering by fast search and Find of Density Peaks (CFDP) can automatically identify the cluster centers without an iterative process. The key step in CFDP is searching for the nearest neighbor with higher density for each point. However, the CFDP algorithm may not be effective for cases in which there exist density fluctuations within a cluster or between two nearby clusters. In this study, two improved algorithms named CFDP-ED-TSNN1 and CFDP-ED-TSNN2 are presented, which adopt different ways to utilize the dissimilarity. Here, the dissimilarity is based on shared nearest neighbors and transitive closure. The experimental results on both several artificial datasets and a real-world dataset show that the improved algorithms are competitive.

AB - A recently proposed clustering algorithm named Clustering by fast search and Find of Density Peaks (CFDP) can automatically identify the cluster centers without an iterative process. The key step in CFDP is searching for the nearest neighbor with higher density for each point. However, the CFDP algorithm may not be effective for cases in which there exist density fluctuations within a cluster or between two nearby clusters. In this study, two improved algorithms named CFDP-ED-TSNN1 and CFDP-ED-TSNN2 are presented, which adopt different ways to utilize the dissimilarity. Here, the dissimilarity is based on shared nearest neighbors and transitive closure. The experimental results on both several artificial datasets and a real-world dataset show that the improved algorithms are competitive.

KW - Clustering

KW - Shared nearest neighbors

KW - Transitive closure

UR - https://www.scopus.com/pages/publications/85031405102

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DO - 10.1007/978-3-319-67274-8_8

M3 - 会议稿件

AN - SCOPUS:85031405102

SN - 9783319672731

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 79

EP - 93

BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Revised Selected Papers

A2 - Moon, Yang-Sae

A2 - Kang, U

A2 - Yu, Jeffrey Xu

A2 - Lim, Ee-Peng

PB - Springer Verlag

T2 - 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 held in conjuction with the Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2017, Workshop on Biologically Inspired Data-Mining Techniques, BDM 2017, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2017 and Workshop on Data Mining in Business Process Management, DM-BPM 2017

Y2 - 23 May 2017 through 23 May 2017

ER -

Ni L, Luo W, Bu C, Hu Y. Improved CFDP algorithms based on shared nearest neighbors and transitive closure. In Moon YS, Kang U, Yu JX, Lim EP, editors, Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Revised Selected Papers. Springer Verlag. 2017. p. 79-93. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67274-8_8

Improved CFDP algorithms based on shared nearest neighbors and transitive closure

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