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K-modes and Fuzzy C-means with modified Particle Swam Optimization Clustering Algorithm for Epilepsy Seizure Data

C.V. Banupriya1 , D. Deviaruna2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 73-77, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.7377

Online published on Jan 31, 2019

Copyright © C.V. Banupriya, D. Deviaruna . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: C.V. Banupriya, D. Deviaruna, “K-modes and Fuzzy C-means with modified Particle Swam Optimization Clustering Algorithm for Epilepsy Seizure Data,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.73-77, 2019.

MLA Style Citation: C.V. Banupriya, D. Deviaruna "K-modes and Fuzzy C-means with modified Particle Swam Optimization Clustering Algorithm for Epilepsy Seizure Data." International Journal of Computer Sciences and Engineering 7.1 (2019): 73-77.

APA Style Citation: C.V. Banupriya, D. Deviaruna, (2019). K-modes and Fuzzy C-means with modified Particle Swam Optimization Clustering Algorithm for Epilepsy Seizure Data. International Journal of Computer Sciences and Engineering, 7(1), 73-77.

BibTex Style Citation:
@article{Banupriya_2019,
author = {C.V. Banupriya, D. Deviaruna},
title = {K-modes and Fuzzy C-means with modified Particle Swam Optimization Clustering Algorithm for Epilepsy Seizure Data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {73-77},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3464},
doi = {https://doi.org/10.26438/ijcse/v7i1.7377}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.7377}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3464
TI - K-modes and Fuzzy C-means with modified Particle Swam Optimization Clustering Algorithm for Epilepsy Seizure Data
T2 - International Journal of Computer Sciences and Engineering
AU - C.V. Banupriya, D. Deviaruna
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 73-77
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

Epilepsy is a stable neurological disorder of the brain, described by regular seizures, i.e., irregular activities. Seizure is the most imperative signal of epilepsy, which is solitary of the most expected neurological disorders. An electroencephalogram (EEG) is a test out used to weigh up the electrical activity in the brain, and is widely used in the recognition and study of epileptic seizures. Hence, it is decisive to develop a quantitative technique to automatically clustering the normal and epileptic brain activities. Several techniques have been developed for unbending out the important features of seizures present in EEGs. The proposed approach is evaluated an extracting the features of EEG signals using wavelet transform coefficients and unsupervised learning technique like clustering the data using Fuzzy C- Means with Modified Particle Swarm Optimization (PSO) and K- Mode Clustering. The recital of the Clusters are analyzed and examined that Fuzzy C-Means with PSO less error rate and out performs than K-Mode Clustering in accuracy.

Key-Words / Index Term

Fuzzy C-means, K-mode, EEG, Seizures, Wavelet, PSO, Clustering

References

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