Hybridization of Fuzzy Label Propagation and Local Resultant Evidential Clustering Method for Cancer Detection

Hybridization of Fuzzy Label Propagation and Local Resultant Evidential Clustering Method for Cancer Detection
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : M. Aruna, S. Sukumaran, V. Srinivasan
DOI : 10.14445/22315381/IJETT-V70I9P204

How to Cite?

M. Aruna, S. Sukumaran, V. Srinivasan, "Hybridization of Fuzzy Label Propagation and Local Resultant Evidential Clustering Method for Cancer Detection," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 34-46, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P204

Abstract
Clustering is a data analysis technique that divides information into numerous homogeneous groups. They are clustering algorithms like Centroid, Density-based Distribution and Hierarchical based Clustering. These algorithms provide better performance for only spherical clusters and acquire high-time complexity issues. So, a Belief-Peaks Evidential Clustering (BPEC) method is efficiently used to deal with non-spherical clusters and improve the clustering performance. However, if the number of clusters is too great, the complexity of BPEC becomes exorbitant. Motivated by these challenges, a hybrid of Fuzzy Label Propagation and Local Resultant Evidential Clustering Method (FLPLRECM) is proposed to handle the sparse and helps to form more precise clusters. Initially, to select Cluster Centres (CCs) based on data dispersion and local density, an adaptive CCs selection approach is proposed. This model provides a Symmetric Neighborhood Graph (SNG) to all Data Points (DPs) with other points along with the standard deviation (SD)/kurtosis, local densities of each point are computed by utilizing the reverse k-Nearest Neighbors (k-NN). To deal with the high dimensional dataset, the Centrality (CE) and Coordination (CO) metrics are introduced to classify the DPs as interior points (ips), inner boundary points (ibps), boundary points (bps), or noise DPs for improving the cluster formation. The intensities and orientations of DPs near the fuzzy CCs and at the fuzzy cluster boundaries are assessed. First, a helpful initial fuzzy cluster assignment is made for each remaining point based on the distances between each CC and its neighbours. After then, neighbour’s labels are used to refine each point's own till the fuzzy partition remains the same. The developed method will provide more precise clusters with less computational time, efficiently used for the analysis of the cancer detection system.

Keywords
Belief-Peak based clustering, Centrality, Coordination, Symmetric Neighborhood Graph, K nearest neighbour.

Reference
[1] D. Jaeger, J. Barth, A. Niehues and C. Fufezan, Pygcluster, “A Novel Hierarchical Clustering Approach, “ Bioinformatics, vol. 30, no.6, pp. 896-898, 2014.
[2] A. Dharmarajan and T. Velmurugan, “Applications of Partition-Based Clustering Algorithms: A Survey,” In 2013 IEEE International Conference on Computational Intelligence and Computing Research, IEEE, pp.1-5, 2013.
[3] F. D. A. De Carvalho Y. Lechevallier and F. M. De Melo, “Partitioning Hard Clustering Algorithms Based on Multiple Dissimilarity Matrices, Pattern Recognition,” vol.45, no.1, pp. 447-464, 2012.
[4] K. V. Rajkumar, A. Yesubabu and K. Subrahmanyam, “Fuzzy Clustering and Fuzzy C-Means Partition Cluster Analysis and Validation Studies on aSubset of Citescore Dataset,” International Journal of Electrical & Computer Engineering, vol. 9, no.4, pp. 2088-8708, 2019.
[5] P. Lingras and G. Peters, “Applying Rough Set Concepts To Clustering, In Rough Sets: Selected Methods and Applications in Management and Engineering,” Springer, London, pp.23-27, 2012.
[6] M. B. Ferraro and P. Giordani, “Possibilistic and Fuzzy Clustering Methods for Robust Analysis of Non-Precise Data,” International Journal of Approximate Reasoning, vol.88, pp.23-38, 2017.
[7] T. Denœux and M. H. Masson, “Evclus: Evidential Clustering of Proximity Data,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.34, no.1, pp. 95-109, 2004.
[8] A. Rodriguez and A. Laio, “Clustering By Fast Search and Find of Density Peaks,” Science, vol.344, no.6191, pp.1492-1496, 2014.
[9] F. Fang, L. Qiu and S. Yuan, “Adaptive Core Fusion-Based Density Peak Clustering for Complex Data with Arbitrary Shapes and Densities, Pattern Recognition,” vol.107, pp.107452, 2020.
[10] J. Hou, A. Zhang and N. Qi, “Density Peak Clustering Based on Relative Density Relationship,” Pattern Recognition, vol.108 , pp.107554, 2020.
[11] A. Lotfi, P. Moradi and H. Beigy, “Density Peaks Clustering Based on Density Backbone and Fuzzy Neighborhood,” Pattern Recognition, vol. 107, pp.107449, 2020.
[12] Z. G. Su and T. Denoeux, “Bpec: Belief-Peaks Evidential Clustering,” IEEE Transactions on Fuzzy Systems, vol. 27, no.1, pp. 111- 123, 2018.
[13] G. Shafer, “A Mathematical Theory of Evidence, “ Princeton University Press, 1976.
[14] Z. G. Liu, J. Dezert, Q. Pan and Y. M. Cheng, “A New Evidential C-Means Clustering Method,” In 2012 15th International Conference on Information Fusion, IEEE, pp. 239-246, 2012.
[15] Y. Aboubi, H. Drias, and N. Kamel, “Bat-Clara: Bat-Inspired Algorithm for Clustering Large Applications, Ifac-Papersonline,” vol. 49, no.12, pp.243-248, 2016.
[16] N. Kaur, and N. Ojha, “Robust Fuzzy Based Clustering Approach in Data Mining Using on Call Data Records,” In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, pp. 1111-1117, 2017.
[17] J. Chang, L. Wang, G. Meng, S. Xiang and C. Pan, “Deep Adaptive Image Clustering,” In Proceedings of the IEEE International Conference on Computer Vision, pp. 5879-5887, 2017.
[18] L. Wang, X. Liu, M. Sun, J. Qu and Y. Wei, “A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems,” Mathematical Problems In Engineering, 2018.
[19] K. Zhou, A. Martin, Q. Pan and Z. Liu, Selp: “Semi-Supervised Evidential Label Propagation Algorithm for Graph Data Clustering,” International Journal of Approximate Reasoning, vol.92 , pp.139-154, 2018.
[20] G. S. Narayana and D. Vasumathi, “An Attributes Similarity-Based K-Medoids Clustering Technique in Data Mining,” Arabian Journal for Science and Engineering, vol.43, no.8, vol. 3979-3992, 2018.
[21] N. Pang, J. Zhang, C. Zhang and X. Qin, “Parallel Hierarchical Subspace Clustering of Categorical Data,” IEEE Transactions on Computers, vol. 68, no.4, pp. 542-555, 2018.
[22] W. Budiaji and F. Leisch, “Simple K-Medoids Partitioning Algorithm for Mixed Variable Data,” Algorithms, vol.12, no.9, pp.177, 2019.
[23] Y. Ping, B. Hao, H. Li, Y. Lai, C. Guo, H. Ma,.. and X. Hei, “Efficient Training Support Vector Clustering With Appropriate Boundary Information,” IEEE Access, vol.7, pp.146964-146978, 2019.
[24] J. Meng, D. Fu and Y. Tang, “Belief-Peaks Clustering Based on Fuzzy Label Propagation,” Applied Intelligence, vol.50, no.4, pp.1259-1271, 2020.
[25] S. Sieranoja and P. Fränti, “Fast and General Density Peaks Clustering,” Pattern Recognition Letters, vol.128, pp.551-558, 2019.
[26] O. Chapelle, M. Chi and A. Zien, A, “A Continuation Method for Semi-Supervised Svms,” In Proceedings of the 23rd International Conference on Machine Learning, pp.185-192, 2006.
[27] T. Yang, D. Fu and X. Li, “Semi-Supervised Classification of Multiple Kernels Embedding Manifold Information,” Cluster Computing, vol.20, no.4, pp. 3417-3426, 2017.
[28] T. Denoeux and O. Kanjanatarakul, “Evidential Clustering: A Review, in Integrated Uncertainty in Knowledge Modelling and Decision Making - 5th International Symposium,” Iukm 2016, pp.24–35, 2016.
[29] D. Liu, H. Y. Bai, H. J. Li and W. J. Wang, “Semi-Supervised Community Detection Using Label Propagation,” International Journal of Modern Physics B, vol.28, no.29 , pp.1450208, 2014.
[30] J. Yu and S. B. Kim, “Consensus Rate-Based Label Propagation for Semi-Supervised Classification,” Information Science, vol. 465, pp. 265 – 284, 2018.
[31] G. H. Golub and C. F, “Van Loan, Matrix Computations, Baltimore.,” Md: Jhu Press, vol. 3 , 2012.