A clustering algorithm based on minimum spanning tree with E-learning applications

Siyang WANG; Zeping TANG; Yanghui RAO; Haoran XIE; Fu Lee WANG

doi:10.1007/978-3-319-32865-2_1

A clustering algorithm based on minimum spanning tree with E-learning applications

Siyang WANG, Zeping TANG, Yanghui RAO^*, Haoran XIE, Fu Lee WANG

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

2 Citations (Scopus)

Abstract

The rapid development of web-based learning applications has generated large amounts of learning resources. Faced with this situation, clustering is valuable to group modeling and intelligent tutoring. In traditional clustering algorithms, the initial centroid of each cluster is often assigned randomly. Sometimes it is very difficult to get an effective clustering result. In this paper, we propose a new clustering algorithm based on a minimum spanning tree, which includes the elimination and construction processes. In the elimination phase, the Euclidean distance is used to measure the density. Objects with low densities are considered as noise and eliminated. In the construction phase, a minimum spanning tree is constructed to choose the initial centroid based on the degree of freedom. Extensive evaluations using datasets with different properties validate the effectiveness of the proposed clustering algorithm. Furthermore, we study how to employ the clustering algorithms in three different e-learning applications.

Original language	English
Title of host publication	Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers
Editors	Di ZOU, Zhiguo GONG, Dickson K.W. CHIU
Place of Publication	Switzerland
Publisher	Springer-Verlag GmbH and Co. KG
Pages	3-12
Number of pages	10
Volume	9584
ISBN (Electronic)	9783319328652
ISBN (Print)	9783319328645
DOIs	https://doi.org/10.1007/978-3-319-32865-2_1
Publication status	Published - 2016
Externally published	Yes
Event	The 14th International Conference on Web-based Learning - Guangzhou, China Duration: 5 Nov 2015 → 8 Nov 2015 http://www.cityu.edu.hk/merc/icwl/icwl2015home.htm

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9584 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 14th International Conference on Web-based Learning
Abbreviated title	ICWL 2015
Country/Territory	China
City	Guangzhou
Period	5/11/15 → 8/11/15
Internet address	http://www.cityu.edu.hk/merc/icwl/icwl2015home.htm

Keywords

Clustering
Density
E-learning
Minimum spanning tree

Access to Document

10.1007/978-3-319-32865-2_1

Cite this

WANG, S., TANG, Z., RAO, Y., XIE, H., & WANG, F. L. (2016). A clustering algorithm based on minimum spanning tree with E-learning applications. In D. ZOU, Z. GONG, & D. K. W. CHIU (Eds.), Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers (Vol. 9584, pp. 3-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9584 LNCS). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-319-32865-2_1

WANG, Siyang ; TANG, Zeping ; RAO, Yanghui et al. / A clustering algorithm based on minimum spanning tree with E-learning applications. Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers. editor / Di ZOU ; Zhiguo GONG ; Dickson K.W. CHIU. Vol. 9584 Switzerland : Springer-Verlag GmbH and Co. KG, 2016. pp. 3-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A clustering algorithm based on minimum spanning tree with E-learning applications",

abstract = "The rapid development of web-based learning applications has generated large amounts of learning resources. Faced with this situation, clustering is valuable to group modeling and intelligent tutoring. In traditional clustering algorithms, the initial centroid of each cluster is often assigned randomly. Sometimes it is very difficult to get an effective clustering result. In this paper, we propose a new clustering algorithm based on a minimum spanning tree, which includes the elimination and construction processes. In the elimination phase, the Euclidean distance is used to measure the density. Objects with low densities are considered as noise and eliminated. In the construction phase, a minimum spanning tree is constructed to choose the initial centroid based on the degree of freedom. Extensive evaluations using datasets with different properties validate the effectiveness of the proposed clustering algorithm. Furthermore, we study how to employ the clustering algorithms in three different e-learning applications.",

keywords = "Clustering, Density, E-learning, Minimum spanning tree",

author = "Siyang WANG and Zeping TANG and Yanghui RAO and Haoran XIE and WANG, {Fu Lee}",

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WANG, S, TANG, Z, RAO, Y, XIE, H & WANG, FL 2016, A clustering algorithm based on minimum spanning tree with E-learning applications. in D ZOU, Z GONG & DKW CHIU (eds), Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers. vol. 9584, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9584 LNCS, Springer-Verlag GmbH and Co. KG, Switzerland, pp. 3-12, The 14th International Conference on Web-based Learning , Guangzhou, China, 5/11/15. https://doi.org/10.1007/978-3-319-32865-2_1

A clustering algorithm based on minimum spanning tree with E-learning applications. / WANG, Siyang; TANG, Zeping; RAO, Yanghui et al.
Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers. ed. / Di ZOU; Zhiguo GONG; Dickson K.W. CHIU. Vol. 9584 Switzerland: Springer-Verlag GmbH and Co. KG, 2016. p. 3-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9584 LNCS).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - A clustering algorithm based on minimum spanning tree with E-learning applications

AU - WANG, Siyang

AU - TANG, Zeping

AU - RAO, Yanghui

AU - XIE, Haoran

AU - WANG, Fu Lee

PY - 2016

Y1 - 2016

N2 - The rapid development of web-based learning applications has generated large amounts of learning resources. Faced with this situation, clustering is valuable to group modeling and intelligent tutoring. In traditional clustering algorithms, the initial centroid of each cluster is often assigned randomly. Sometimes it is very difficult to get an effective clustering result. In this paper, we propose a new clustering algorithm based on a minimum spanning tree, which includes the elimination and construction processes. In the elimination phase, the Euclidean distance is used to measure the density. Objects with low densities are considered as noise and eliminated. In the construction phase, a minimum spanning tree is constructed to choose the initial centroid based on the degree of freedom. Extensive evaluations using datasets with different properties validate the effectiveness of the proposed clustering algorithm. Furthermore, we study how to employ the clustering algorithms in three different e-learning applications.

AB - The rapid development of web-based learning applications has generated large amounts of learning resources. Faced with this situation, clustering is valuable to group modeling and intelligent tutoring. In traditional clustering algorithms, the initial centroid of each cluster is often assigned randomly. Sometimes it is very difficult to get an effective clustering result. In this paper, we propose a new clustering algorithm based on a minimum spanning tree, which includes the elimination and construction processes. In the elimination phase, the Euclidean distance is used to measure the density. Objects with low densities are considered as noise and eliminated. In the construction phase, a minimum spanning tree is constructed to choose the initial centroid based on the degree of freedom. Extensive evaluations using datasets with different properties validate the effectiveness of the proposed clustering algorithm. Furthermore, we study how to employ the clustering algorithms in three different e-learning applications.

KW - Clustering

KW - Density

KW - E-learning

KW - Minimum spanning tree

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M3 - Conference paper (refereed)

AN - SCOPUS:84981225412

SN - 9783319328645

VL - 9584

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

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EP - 12

BT - Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers

A2 - ZOU, Di

A2 - GONG, Zhiguo

A2 - CHIU, Dickson K.W.

PB - Springer-Verlag GmbH and Co. KG

CY - Switzerland

T2 - The 14th International Conference on Web-based Learning

Y2 - 5 November 2015 through 8 November 2015

ER -

WANG S, TANG Z, RAO Y, XIE H, WANG FL. A clustering algorithm based on minimum spanning tree with E-learning applications. In ZOU D, GONG Z, CHIU DKW, editors, Current Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers. Vol. 9584. Switzerland: Springer-Verlag GmbH and Co. KG. 2016. p. 3-12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2016 Aug 2. doi: 10.1007/978-3-319-32865-2_1

A clustering algorithm based on minimum spanning tree with E-learning applications

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