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

© 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

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.

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

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