Modified Fuzzy C-Means Clustering for Anomaly Detection in Bio-medical Data.

Authors

  • Srikanta Kumar Sahoo ITER, SOA deemed to be university, Bhubaneswar-751030, India
  • Priyabrata Pattanaik ITER, SOA deemed to be university, Bhubaneswar-751030, India
  • Mihir Narayan Mohanty ITER, SOA deemed to be university, Bhubaneswar-751030, India

Keywords:

Anomaly detection, Fuzzy-c-means, Opposition based learning, Outlier detection, Principal component analysis

Abstract

Bio-medical data for different diseases are always with noise and outliers. As the source and medium differs from place to place and time to time it happens to be noisy. In this work, the authors have tried to analyse biomedical data statistically using principal component analysis. Here, the fuzzy centroid is modified with opposition learning based algorithm. Due to optimal algorithms, the modified fuzzy c-means utilized for clustering that performs excellent in terms of outlier detection. The data taken from UCI machine learning repository are of classification type. It is shown in the result section that the outliers have been detected successfully.

Downloads

Download data is not yet available.

References

Aggarwal CC, Aggarwal CC. An introduction to outlier analysis. Springer International Publishing; 2017.

Ketepalli G, Tata S, Vaheed S, Srikanth YM. Anomaly Detection in Credit Card Transaction using Deep Learning Techniques. In2022 7th International Conference on Communication and Electronics Systems (ICCES) 2022 Jun 22 (pp. 1207-1214). IEEE.

Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM computing surveys (CSUR). 2009 Jul 30;41(3):1-58.

Barnett V, Lewis T. Outliers in statistical data. New York: Wiley; 1994 Apr.

Yang P, Huang B. KNN based outlier detection algorithm in large dataset. In 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing 2008 Dec 21 (Vol. 1, pp. 611-613). IEEE.

Jiang SY, An QB. Clustering-based outlier detection method. In 2008 Fifth international conference on fuzzy systems and knowledge discovery 2008 Oct 18 (Vol. 2, pp. 429-433). IEEE.

Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. Inkdd 1996 Aug 2 (Vol. 96, No. 34, pp. 226-231).

Smith R, Bivens A, Embrechts M, Palagiri C, Szymanski B. Clustering approaches for anomaly-based intrusion detection. Proceedings of intelligent engineering systems through artificial neural networks. 2002 Oct;9.

He Z, Xu X, Deng S. Discovering cluster-based local outliers. Pattern recognition letters. 2003 Jun 1;24(9-10):1641-50.

Elmogy A, Rizk H, Sarhan AM. Ofcod: On-the-fly clustering-based outlier detection framework. Data. 2020 Dec 30;6(1):1.

Degirmenci A, Karal O. Efficient density and cluster-based incremental outlier detection in data streams. Information Sciences. 2022 Aug 1;607:901-20.

Mazarbhuiya FA, Shenify M. A Mixed Clustering Approach for Real-Time Anomaly Detection. Applied Sciences. 2023 Mar 24;13(7):4151.

Jiang SY, An QB. Clustering-based outlier detection method. In 2008 Fifth international conference on fuzzy systems and knowledge discovery 2008 Oct 18 (Vol. 2, pp. 429-433). IEEE.

He Z, Deng S, Xu X. An optimization model for outlier detection in categorical data. In International conference on intelligent computing 2005 Aug 23 (pp. 400-409). Berlin, Heidelberg: Springer Berlin Heidelberg.

He Z, Deng S, Xu X, Huang JZ. A fast greedy algorithm for outlier mining. In Advances in Knowledge Discovery and Data Mining: 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006. Proceedings 10 2006 (pp. 567-576). Springer Berlin Heidelberg.

Vanem E, Brandsæter A. Cluster-based anomaly detection in condition monitoring of a marine engine system. In2018 Prognostics and System Health Management Conference (PHM-Chongqing) 2018 Oct 26 (pp. 20-31). IEEE.

Li J, Izakian H, Pedrycz W, Jamal I. Clustering-based anomaly detection in multivariate time series data. Applied Soft Computing. 2021 Mar 1;100:106919.

Kim YG, Lee KM. Association-based outlier detection for mixed data. Indian Journal of Science and Technology. 2015 Oct;8(25):1-6.

Lan DT, Yoon S. Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement. Sensors. 2023 Mar 21;23(6):3318.

Gadal S, Mokhtar R, Abdelhaq M, Alsaqour R, Ali ES, Saeed R. Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization. Electronics. 2022 Jul 10;11(14):2158.

Wang D, Shen Z, Wu W. A fuzzy clustering based anomaly node detection method for publish/subscribe distributed systems. InJournal of Physics: Conference Series 2021 Feb 1 (Vol. 1813, No. 1, p. 012046). IOP Publishing.

Du H, Ye Q, Sun Z, Liu C, Xu W. FAST-ODT: A lightweight outlier detection scheme for categorical data sets. IEEE Transactions on Network Science and Engineering. 2020 Sep 9;8(1):13-24.

Lin H, Li Z. Outlier detection for set-valued data based on rough set theory and granular computing. International Journal of General Systems. 2023 May 19;52(4):385-413.

Pearson K. LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science. 1901 Nov 1;2(11):559-72.

Smith LI. A tutorial on principal components analysis. 2002.

Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers & geosciences. 1984 Jan 1;10(2-3):191-203.

Sahoo SK, Pattanaik P, Mohanty MN, Mishra DK. Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering. International Journal of Advanced Computer Science and Applications. 2023;14(4).

Sahoo SK, Pattanaik P, Mohanty MN. Modified bee colony optimization with opposition learning algorithm on use of medical data clustering. Intelligent Decision Technologies.(Preprint):1-6.

Downloads

Published

23.02.2024

How to Cite

Sahoo, S. K. ., Pattanaik, P. ., & Mohanty, M. N. . (2024). Modified Fuzzy C-Means Clustering for Anomaly Detection in Bio-medical Data. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 519–526. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4917

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.