Improved Intelligent Techniques of Ensemble Data Clustering Method Using Bees Swarm Optimization Ensemble Approach
DOI:
https://doi.org/10.61841/k8trxa08Keywords:
Clustering, Bees Swarm Optimization, Cluster Ensemble, Intelligent Data AnalyticsAbstract
Clustering is considered an unsupervised partitioning method, which is an intelligent self-organizing system that partitions the datasets in a comparable or a different way, where each data cluster consists of similar data points. As of late, the clustering ensemble is regarded as a solution to extract the categorical data points into relevant clusters in a more effective way. However, it encounters a serious problem related to data imperfection during data partitioning into clusters. The present examination thinks about this as the primary issue and improves it using the following thought. Right now the ensemble is clustering over clear-cut datasets using a Bee Swarm Optimization (BSO)-based cluster ensemble approach. The similarity measurement is carried out using entropy-weighted triple quality to find the similarity difference between the clusters. The knowledge paradigms, including cognitive science and systems, are intended to improve the clustering quality over categorical datasets. The result shows that the proposed method is more accurate than existing methods over categorical datasets in terms of clustering accuracy, normalized mutual information, and adjusted rand-based. This shows the effectiveness of the BSO ensemble clustering algorithm more than the existing link clustering ensemble algorithm.
Downloads
References
[1] Agus Yogeswara Wibawa, I.M., & Wiksuana, I.G.B. (2019). Performance analysis of pharmaceutical
installation as basic of feasibility: a self-based business unit into general services agency. International
Research Journal of Management, IT and Social Sciences, 6(6), 229-238.
[2] Akbari, R., Mohammadi, A., & Ziarati, K. (2010). A novel bee swarm optimization algorithm for
numerical function optimization. Communications in Nonlinear Science and Numerical Simulation, 15(10),
3142-3155.
[3] Al-Razgan, M., Domeniconi, C., & Barbará, D. (2008). Random subspace ensembles for clustering
categorical data. In Supervised and unsupervised ensemble methods and their applications, (pp. 31-48).
Springer, Berlin, Heidelberg.
[4] Anandakumar, H., & Umamaheswari, K. (2017). An Efficient Optimized Handover in Cognitive Radio
Networks using Cooperative Spectrum Sensing. Intelligent Automation & Soft Computing, 1–8.
doi:10.1080/10798587.2017.1364931
[5] Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive
radio networks during cooperative spectrum handovers. Cluster Computing, 20(2), 1505–1515.
[6] Anandakumar, H., & Umamaheswari, K. (2018). A bio-inspired swarm intelligence technique for social
aware cognitive radio handovers. Computers & Electrical Engineering, 71, 925–937.
[7] Ananthanarayanan, D., & Kannan, D. (2016). Critical Appraisal by students on faculty-Issues and
feedback. International Journal of Advanced Engineering Research and Science, 3(3), 94-98.
[8] Arulmurugan, R., & Anandakumar, H. (2018). Early Detection of Lung Cancer Using Wavelet Feature
Descriptor and Feed Forward Back Propagation Neural Networks Classifier. Lecture Notes in
Computational Vision and Biomechanics, 103–110.
[9] Ayad, H.G., & Kamel, M.S. (2007). Cumulative voting consensus method for partitions with variable
number of clusters. IEEE transactions on pattern analysis and machine intelligence, 30(1), 160-173.
[10] Dandotiya, S., & Ramaiya, P. (2016). A Review of ECG Signal De-noising and Peaks Detection
Techniques. International Journal of Advanced Engineering Research and Science, 3(3), 91-93.
[11] Desfiandi, A., Suman Rajest, S., S. Venkateswaran, P., Palani Kumar, M., & Singh, S. (2019). Company
Credibility: A Tool to Trigger Positive Csr Image in the Cause-Brand Alliance Context in Indonesia.
Humanities & Social Sciences Reviews, 7(6), 320-331.
[12] Dr. P. Suresh and Suman Rajest S (2019), “An Analysis of Psychological Aspects in Student-Centered Learning Activities and Different Methods” in Journal of International Pharmaceutical Research, Volume:46, Issue 01, Page No.: 165-172.
[13] Dr.P.S. Venkateswaran, Dr. A. Sabarirajan, S. Suman Rajest And R. Regin (2019) “The Theory of the Postmodernism in Consumerism, Mass Culture and Globalization” in The Journal of Research on the Lepidoptera Volume 50 (4): 97-113
[14] Dr.M.Kannan, D., J.Sujatha, J., & Dr.N.R. Ananthanarayanan, D. (2016). Priority Analysis on technical teaching faculty over the selection of ICT based Seminars, Conferences, Workshop and Symposium for their career prospective. International Journal of Advanced Engineering Research and Science, 3(3), 99-103.
[15] Haldorai, A., & Kandaswamy, U. (2019). Supervised Machine Learning Techniques in Intelligent Network Handovers. EAI/Springer Innovations in Communication and Computing, 135–154. doi:10.1007/978-3-030-15416-5_7
[16] Haldorai, A., Ramu, A., & Murugan, S. (2018). Social Aware Cognitive Radio Networks. Social Network Analytics for Contemporary Business Organizations, 188–202. doi:10.4018/978-1-5225-5097-6.ch010
[17] He, Z., Xu, X., & Deng, S. (2005). A cluster ensemble method for clustering categorical data. Information
Fusion, 6(2), 143-151.
[18] Iam-On, N., & Boongoen, T. (2015). Diversity-driven generation of link-based cluster ensemble and
application to data classification. Expert Systems with Applications, 42(21), 8259-8273.
[19] Iam-on, N., & Garrett, S. (2010). Linkclue: A mat lab package for link-based cluster ensembles. Journal of
Statistical Software, 36(9), 1-36.
[20] Iam-On, N., Boongeon, T., Garrett, S., & Price, C. (2012). A link -based cluster ensemble approach for
categorical data clustering. IEEE Transactions on knowledge and data engineering, 24(3), 413-425.
[21] Iam-On, N., Boongoen, T., & Garrett, S. (2010). LCE: a link-based cluster ensemble method for improved
gene expression data analysis. Bioinformatics, 26(12), 1513-1519.
[22] Iam-On, N., Boongoen, T., Garrett, S., & Price, C. (2011). A link-based approach to the cluster ensemble
problem. IEEE transactions on pattern analysis and machine intelligence, 33(12), 2396-2409.
[23] J.T. Mentzer, W. DeWitt, J. S. Keebler et al., “Defining supply chain management,” Journal of Business
Logistics, vol. 22, no. 2, pp. 1–25, 2001.
[24] Jia, J., Xiao, X., Liu, B., & Jiao, L. (2011). Bagging-based spectral clustering ensemble selection. Pattern
Recognition Letters, 32(10), 1456-1467.
[25] K.B. Adanov, S. Suman Rajest, Mustagaliyeva Gulnara, Khairzhanova Akhmaral (2019), “A Short View
on the Backdrop of American’s Literature”. Journal of Advancd Research in Dynamical and Control
Systems, Vol. 11, No. 12, pp. 182-192.
[26] K.H. Chen, K.J. Wang, M.L. Tsai et al., “Gene selection for cancer identification: a decision tree model
empowered by particle swarm optimization algorithm,” BMC Bioinformatics, vol. 15, no. 1, article 49,
2014.
[27] L. Altamirano, M.C. Riff, I. Araya, and L. Trilling, “Anesthesiology nurse scheduling using particle swarm
optimization,” International Journal of Computational Intelligence Systems, vol. 5, no. 1, pp. 111–125,
2012.
[28] L.Y. Chuang, H.Y. Lane, Y.D. Lin, M.T. Lin, C.H. Yang, and H.-W. Chang, “Identification of SNP
barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization
algorithm,” Annals of General Psychiatry, vol. 13, article 15, 2014.
[29] M. Karabulut and T. Ibrikci, “A Bayesian Scoring Scheme based Particle Swarm Optimization algorithm to
identify transcription factor binding sites,” Applied Soft Computing Journal, vol. 12, no. 9, pp. 2846–2855,
2012.
[30] M. Mandal and A. Mukhopadhyay, “A graph-theoretic approach for identifying non-redundant and relevant
gene markers from microarray data using multiobjective binary PSO,” PLoS ONE, vol. 9, no. 3, Article ID
e90949, 2014.
[31] M. Salahi, A. Jamalian, and A. Taati, “Global minimization of multi-funnel functions using particle swarm
optimization,” Neural Computing and Applications, vol. 23, no. 7-8, pp. 2101–2106, 2013.
[32] Marrybeth, J.K., Brackz, J.S., Hadson, W.B., & Zlong, M.S. (2019). Talent acquisition and talent
engagement practices significant impact over employee satisfaction. International Research Journal of
Management, IT and Social Sciences, 6(6), 244-252.
[33] Md. Salamun Rashidin, Sara Javed, Bin Liu, Wang Jian, Suman Rajest S (2019), “Insights: Rivals
Collaboration on Belt and Road Initiatives and Indian Recourses” in Journal of Advanced Research in
Dynamical and Control Systems, Vol. 11, SI. 04, Page No.: 1509-1522.
[34] Minaei-Bidgoli, B., Parvin, H., Alinejad-Rokny, H., Alizadeh, H., & Punch, W.F. (2014). Effects of
resampling method and adaptation on clustering ensemble efficacy. Artificial Intelligence Review, 41(1),
27-48.
[35] P.J. García Nieto, E. García-Gonzalo, J.R. Alonso Fernández, and C. Díaz Muñiz, “Hybrid PSO-SVMbased method for long-term forecasting of turbidity in the Nalón river basin: a case study in Northern Spain,” Ecological Engineering, vol. 73, pp. 192–200, 2014.
[36] Pawar, K., Gawande, S. and V Godase, D. (2016). Experimental Parametric Study of Biodiesel to Develop Economic Zero Effluent Discharge (ZED) for Diesel System. International Journal of Advanced Engineering, Management and Science, 2(4), pp.68-71.
[37] Piedade, S.D.R., Wardana, I.M., Riana, G., & Dewi, I.G.A.M. (2019). The role of motivation: the effect of transformational leadership on employee performance. International Research Journal of Management, IT and Social Sciences, 6(6), 253-263.
[38] Prasad, B., S. Ekambaram, S., D. Vannurappa, D., S. Md.Mazhar-ul-Haq, S. and Basha, S. (2016). A Study
of nature of the Photovoltaic modules under the influence of partial shading effect. International Journal of
Advanced Engineering, Management and Science, 2(4), pp.72-74.
[39] Purastu, P., Kumaran, M. and Bhattacharyya, S. (2016). Multibiometric Authentication System Processed
by the Use of Fusion Algorithm. International Journal of Advanced Engineering, Management and Science,
2(4), pp.62-67.
[40] Rajest, S.S., Suresh, D. (2018). The Deducible Teachings of Historiographic Metafiction of Modern
Theories of Both Fiction and History. Eurasian Journal of Analytical Chemistry, 13(4), emEJAC191005.
[41] Suganya, M., & Anandakumar, H. (2013). Handover based spectrum allocation in cognitive radio
networks. 2013 International Conference on Green Computing, Communication and Conservation of
Energy (ICGCE). doi:10.1109/icgce.2013.6823431
[42] Suman Rajest S, Dr. P. Suresh (2018), “Absurd Realism And Structure In Thomas Pynchon’s The Crying
Of Lot 49” in Journal of Advanced Research in Dynamical and Control Systems, Vol.10, SI.11, Page No.:
571-580.
[43] Suman Rajest S, Dr.P. Suresh (2018), “The Problematizing of History Concentrated on The Poetics of
Historiographic Metafiction by Postmodernism and How It Influences Postmodern Fiction” in International
Journal of Pure and Applied Mathematics, Vol.119, SI.16, Page No.: 2457-2469.
[44] Suman Rajest S, Dr. P. Suresh (2018), “Themes and Techniques from Modernism to Postmodernism: The Dubious Continuance of Gravity’s Rainbow” in International Journal of Pure and Applied Mathematics, Vol.119, SI.16, Page No.: 2373-2384.
[45] Suman Rajest S, Dr. P. Suresh (2019), “The Dialog On Postmodernism Intertextuality, Parody, The Talk Of History And The Issue Of Reference” in International Journal of Recent Technology and Engineering, Vol.7, Issue-5C, Page No.: 244-251.
[46] Swamy S, N., Nayak, S. and M N, V. (2016). Analysis on IoT Challenges, Opportunities, Applications and Communication Models. International Journal of Advanced Engineering, Management and Science, 2(4), pp.075-078.
[47] Y.J. Liu, X. Shi, G. Huang, B. Li, and L. Zhao, “Classification of diffuse large B cell lymphoma gene expression data based on two-layer particle swarm optimization,” in Proceedings of the 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '13), pp. 422–427, Shenyang, China, July 2013.
[48] Yogasari, I.A.M., & Budiasih, I.G.A.N. (2019). Impact of organizational justice perceptions and transformational leadership role on counter productive work behavior. International Research Journal of Management, IT and Social Sciences, 6(6), 239-243.
[49] Yuvaraj, N., & Dhas, C.S.G. (2018). High-performance link-based cluster ensemble approach for categorical data clustering. The Journal of Supercomputing, 1-24.
[50] Z. Du, Y. Zhu, and W. Liu, “Combining quantum-behaved PSO and K2 algorithm for enhancing gene network construction,” Current Bioinformatics, vol. 8, no. 1, pp. 133–137, 2013.
[51] Z.G. Zeng, J. Xu, S. Wu, and M. Shen, “Antithetic method-based particle swarm optimization for a queuing network problem with fuzzy data in concrete transportation systems,” Computer-Aided Civil and Infrastructure Engineering, vol. 29, no. 10, pp. 771–800, 2014.
[52] Zaki, M.J., & Peters, M. (2005, April). CLICKS: Mining subspace clusters in categorical data via K-partite maximal cliques. In 21st International Conference on Data Engineering, pp. 355-356.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
