Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection

M. R. Mahmud; M. A. Mamun; M. A. Hossain; M. P. Uddin

doi:10.1109/IC4ME2.2018.8465607

Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection

M. R. Mahmud
, M. A. Mamun
, M. A. Hossain
, M. P. Uddin

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

11 Scopus citations

Abstract

Brain is the most precious part of the human body. Therefore, it is entirely necessary to substantially distinguish the different regions of the brain for diagnosing any anomalies in medical science. Most recently, data mining provides some clustering algorithms for efficiently detecting the diverse area of the brain. In this paper, different clustering algorithms for division display have been studied. The essential thought of clustering is to assign the similarity between the distance, which refers to the data to measure the similarity of the size of the data is ordered until all the data gathering is finished. But the primary point is to demonstrate the examination of the different clustering algorithms to discover which algorithm will be most reasonable for the users.

Original language	English
Title of host publication	International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538647752
DOIs	https://doi.org/10.1109/IC4ME2.2018.8465607
State	Published - 13 Sep 2018
Externally published	Yes
Event	2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018 - Rajshahi, Bangladesh Duration: 8 Feb 2018 → 9 Feb 2018

Publication series

Name	International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018

Conference

Conference	2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018
Country/Territory	Bangladesh
City	Rajshahi
Period	8/02/18 → 9/02/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Bisecting K-means algorithm
Clustering methods
Data Mining
K-means algorithm
Magnetic Resonance Imaging

ASJC Scopus subject areas

Chemical Engineering (miscellaneous)
Computer Science (miscellaneous)
Computer Networks and Communications
Electrical and Electronic Engineering
Mechanics of Materials
Electronic, Optical and Magnetic Materials

Access to Document

10.1109/IC4ME2.2018.8465607

Cite this

Mahmud, M. R., Mamun, M. A., Hossain, M. A., & Uddin, M. P. (2018). Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection. In International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018 Article 8465607 (International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC4ME2.2018.8465607

Mahmud, M. R. ; Mamun, M. A. ; Hossain, M. A. et al. / Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection. International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018).

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title = "Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection",

abstract = "Brain is the most precious part of the human body. Therefore, it is entirely necessary to substantially distinguish the different regions of the brain for diagnosing any anomalies in medical science. Most recently, data mining provides some clustering algorithms for efficiently detecting the diverse area of the brain. In this paper, different clustering algorithms for division display have been studied. The essential thought of clustering is to assign the similarity between the distance, which refers to the data to measure the similarity of the size of the data is ordered until all the data gathering is finished. But the primary point is to demonstrate the examination of the different clustering algorithms to discover which algorithm will be most reasonable for the users.",

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language = "English",

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Mahmud, MR, Mamun, MA, Hossain, MA & Uddin, MP 2018, Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection. in International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018., 8465607, International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018, Institute of Electrical and Electronics Engineers Inc., 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018, Rajshahi, Bangladesh, 8/02/18. https://doi.org/10.1109/IC4ME2.2018.8465607

Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection. / Mahmud, M. R.; Mamun, M. A.; Hossain, M. A. et al.
International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8465607 (International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018).

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

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AU - Uddin, M. P.

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AB - Brain is the most precious part of the human body. Therefore, it is entirely necessary to substantially distinguish the different regions of the brain for diagnosing any anomalies in medical science. Most recently, data mining provides some clustering algorithms for efficiently detecting the diverse area of the brain. In this paper, different clustering algorithms for division display have been studied. The essential thought of clustering is to assign the similarity between the distance, which refers to the data to measure the similarity of the size of the data is ordered until all the data gathering is finished. But the primary point is to demonstrate the examination of the different clustering algorithms to discover which algorithm will be most reasonable for the users.

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Mahmud MR, Mamun MA, Hossain MA, Uddin MP. Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection. In International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8465607. (International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018). doi: 10.1109/IC4ME2.2018.8465607

Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this