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- W4320178903 abstract "Traditional clustering algorithms are not suited for the heterogeneous data of the Sensor-Based Internet of Things. The accuracy of real-time data processing, in such applications, is further compromised because of the noise and missing values in the data. Considering the need for accurate clustering, a graph Laplacian-based heterogeneous data clustering is proposed in this work. Exploiting the correlation structure of the data, weight graphs are used to generate a graph Laplacian matrix to obtain co-related data points. Eigenvalues are further used to obtain distance-based, accurate clusters. The proposed algorithm is validated on five different real-world data sets and is able to outperform most of the existing algorithms. A detailed mathematical analysis followed by extensive simulation on real-world data sets proves the dexterity of the proposed method, as the performance gap, with respect to the state-of-the-art methods, in terms of accuracy and purity is as high as 30%." @default.
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- W4320178903 date "2023-02-12" @default.
- W4320178903 modified "2023-10-18" @default.
- W4320178903 title "Graph Laplacian for Heterogeneous Data Clustering in Sensor-Based Internet of Things" @default.
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- W4320178903 doi "https://doi.org/10.1080/03772063.2023.2173673" @default.
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