Fuzzy Density-Based Clustering for Medical Diagnosis

Syed Muhammad Waqas; Kashif Hussain; Salama A. Mostafa; Nazri Mohd Nawi; Sumra Khan

doi:10.1007/978-3-031-00828-3_26

Fuzzy Density-Based Clustering for Medical Diagnosis

Syed Muhammad Waqas, Kashif Hussain, Salama A. Mostafa, Nazri Mohd Nawi, Sumra Khan

Science and Engineering

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

Abstract

Clustering is an effective technique for identifying patterns and structures in labeled and unlabeled datasets in the medical sector. Density-based clustering is a sophisticated machine learning technique for identifying distinctive patterns in large datasets. However, this approach has certain drawbacks like inability to determine local densities and overlapping clusters or clusters with blur boundaries. This paper embeds fuzzy logic with density-based clustering, for improved clustering separability. In order to validate the usability of the proposed approach, we use five real-world datasets belonging to medical domain.

Original language	English
Title of host publication	Recent Advances in Soft Computing and Data Mining
Subtitle of host publication	Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022
Editors	Rozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris, Jemal H. Abawajy, Nureize Arbaiy
Publisher	Springer
Pages	264-271
Number of pages	8
ISBN (Electronic)	978-3-031-00828-3
ISBN (Print)	978-3-031-00827-6
DOIs	https://doi.org/10.1007/978-3-031-00828-3_26
Publication status	Published - 4 May 2022

Publication series

Name	Lecture Notes in Networks and Systems

Access to Document

10.1007/978-3-031-00828-3_26

Cite this

Waqas, S. M., Hussain, K., Mostafa, S. A., Nawi, NM., & Khan, S. (2022). Fuzzy Density-Based Clustering for Medical Diagnosis. In R. Ghazali, N. M. Nawi, M. M. Deris, J. H. Abawajy, & N. Arbaiy (Eds.), Recent Advances in Soft Computing and Data Mining: Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022 (pp. 264-271). (Lecture Notes in Networks and Systems). Springer. https://doi.org/10.1007/978-3-031-00828-3_26

Waqas, Syed Muhammad ; Hussain, Kashif ; Mostafa, Salama A. et al. / Fuzzy Density-Based Clustering for Medical Diagnosis. Recent Advances in Soft Computing and Data Mining: Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022. editor / Rozaida Ghazali ; Nazri Mohd Nawi ; Mustafa Mat Deris ; Jemal H. Abawajy ; Nureize Arbaiy. Springer, 2022. pp. 264-271 (Lecture Notes in Networks and Systems).

@inproceedings{450fbd42298a46758e04a774a1a2ed05,

title = "Fuzzy Density-Based Clustering for Medical Diagnosis",

abstract = "Clustering is an effective technique for identifying patterns and structures in labeled and unlabeled datasets in the medical sector. Density-based clustering is a sophisticated machine learning technique for identifying distinctive patterns in large datasets. However, this approach has certain drawbacks like inability to determine local densities and overlapping clusters or clusters with blur boundaries. This paper embeds fuzzy logic with density-based clustering, for improved clustering separability. In order to validate the usability of the proposed approach, we use five real-world datasets belonging to medical domain.",

author = "Waqas, {Syed Muhammad} and Kashif Hussain and Mostafa, {Salama A.} and Nazri Mohd Nawi and Sumra Khan",

year = "2022",

month = may,

day = "4",

doi = "10.1007/978-3-031-00828-3_26",

language = "English",

isbn = "978-3-031-00827-6",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "264--271",

editor = "Rozaida Ghazali and Nawi, {Nazri Mohd} and Deris, {Mustafa Mat} and Abawajy, {Jemal H.} and Nureize Arbaiy",

booktitle = "Recent Advances in Soft Computing and Data Mining",

}

Waqas, SM, Hussain, K, Mostafa, SA, Nawi, NM & Khan, S 2022, Fuzzy Density-Based Clustering for Medical Diagnosis. in R Ghazali, NM Nawi, MM Deris, JH Abawajy & N Arbaiy (eds), Recent Advances in Soft Computing and Data Mining: Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022. Lecture Notes in Networks and Systems, Springer, pp. 264-271. https://doi.org/10.1007/978-3-031-00828-3_26

Fuzzy Density-Based Clustering for Medical Diagnosis. / Waqas, Syed Muhammad; Hussain, Kashif; Mostafa, Salama A. et al.
Recent Advances in Soft Computing and Data Mining: Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022. ed. / Rozaida Ghazali; Nazri Mohd Nawi; Mustafa Mat Deris; Jemal H. Abawajy; Nureize Arbaiy. Springer, 2022. p. 264-271 (Lecture Notes in Networks and Systems).

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

TY - GEN

T1 - Fuzzy Density-Based Clustering for Medical Diagnosis

AU - Waqas, Syed Muhammad

AU - Hussain, Kashif

AU - Mostafa, Salama A.

AU - Nawi, Nazri Mohd

AU - Khan, Sumra

PY - 2022/5/4

Y1 - 2022/5/4

N2 - Clustering is an effective technique for identifying patterns and structures in labeled and unlabeled datasets in the medical sector. Density-based clustering is a sophisticated machine learning technique for identifying distinctive patterns in large datasets. However, this approach has certain drawbacks like inability to determine local densities and overlapping clusters or clusters with blur boundaries. This paper embeds fuzzy logic with density-based clustering, for improved clustering separability. In order to validate the usability of the proposed approach, we use five real-world datasets belonging to medical domain.

AB - Clustering is an effective technique for identifying patterns and structures in labeled and unlabeled datasets in the medical sector. Density-based clustering is a sophisticated machine learning technique for identifying distinctive patterns in large datasets. However, this approach has certain drawbacks like inability to determine local densities and overlapping clusters or clusters with blur boundaries. This paper embeds fuzzy logic with density-based clustering, for improved clustering separability. In order to validate the usability of the proposed approach, we use five real-world datasets belonging to medical domain.

U2 - 10.1007/978-3-031-00828-3_26

DO - 10.1007/978-3-031-00828-3_26

M3 - Conference contribution

SN - 978-3-031-00827-6

T3 - Lecture Notes in Networks and Systems

SP - 264

EP - 271

BT - Recent Advances in Soft Computing and Data Mining

A2 - Ghazali, Rozaida

A2 - Nawi, Nazri Mohd

A2 - Deris, Mustafa Mat

A2 - Abawajy, Jemal H.

A2 - Arbaiy, Nureize

PB - Springer

ER -

Waqas SM, Hussain K, Mostafa SA, Nawi NM, Khan S. Fuzzy Density-Based Clustering for Medical Diagnosis. In Ghazali R, Nawi NM, Deris MM, Abawajy JH, Arbaiy N, editors, Recent Advances in Soft Computing and Data Mining: Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022. Springer. 2022. p. 264-271. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-00828-3_26