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Bu, F, Hu, C, Zhang, Q, Bai, C, T. Yang, L and Baker, T (2020) A Cloud-Edge-aided Incremental High-order Possibilistic c-Means Algorithm for Medical Data Clustering. IEEE Transactions on Fuzzy Systems. ISSN 1063-6706
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A Cloud-Edge-aided Incremental High-order Possibilistic c-Means Algorithm for Medical Data Clustering.pdf - Accepted Version Download (865kB) | Preview |
Abstract
Medical Internet of Things are generating a big volume of data to enable smart medicine that tries to offer computer-aided medical and healthcare services with artificial intelligence techniques like deep learning and clustering. However, it is a challenging issue for deep learning and clustering algorithms to analyze large medical data because of their high computational complexity, thus hindering the progress of smart medicine. In this paper, we present an incremental high-order possibilistic c-means algorithm on a cloud-edge computing system to achieve medical data co-clustering of multiple hospitals in different locations. Specifically, each hospital employs the deep computation model to learn a feature tensor of each medical data object on the local edge computing system and then uploads the feature tensors to the cloud computing platform. The high-order possibilistic c-means algorithm (HoPCM) is performed on the cloud system for medical data clustering on uploaded feature tensors. Once the new medical data feature tensors are arriving at the cloud computing platform, the incremental high-order possibilistic c-means algorithm (IHoPCM) is performed on the combination of the new feature tensors and the previous clustering centers to obtain clustering results for the feature tensors received to date. In this way, repeated clustering on the previous feature tensors is avoided to improve the clustering efficiency. In the experiments, we compare different algorithms on two medical datasets regarding clustering accuracy and clustering efficiency. Results show that the presented IHoPCM method achieves great improvements over the compared algorithms in clustering accuracy and efficiency.
| Item Type: | Article |
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| Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Uncontrolled Keywords: | 0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Institute of Electrical and Electronics Engineers |
| Date Deposited: | 23 Nov 2020 12:38 |
| Last Modified: | 04 Sep 2021 06:20 |
| DOI or ID number: | 10.1109/TFUZZ.2020.3022080 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/14065 |
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