Hybrid local diffusion maps and improved cuckoo search algorithm for multiclass dataset analysis

Jia, Bo, Yu, Biting, Wu, Qi, Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556, Wei, Chuanfeng, Law, Rob and Fu, Shan (2016) Hybrid local diffusion maps and improved cuckoo search algorithm for multiclass dataset analysis. Neurocomputing, 189 . pp. 106-116. ISSN 0925-2312 (doi:https://doi.org/10.1016/j.neucom.2015.12.066)

Full text is not in this repository.

Abstract

Data clustering is a meaningful tool that can, help people classify mixed data automatically. With rapid technological development, data in modern applications become large scale and high dimensional. Some original clustering methods are not suitable for complicated datasets. To improve the performance of the popular kernel fuzzy C-means (KFCM), this study proposed a local density adaptive diffusion maps (LDM) technique to obtain a reliable similarity description and dimensionality reduction. To find the valid cluster centroids of the dataset, this study also proposed an improved cuckoo search (ICS) to optimize the unknown parameters of the KFCM model. The ICS algorithm utilized quaternions to represent individuals who will be optimized. Variable step length of Lévy flights and discovery probability were also proposed, which were adjusted by the evolutional ratio of the cuckoo search process. To verify the availability of the ICS, 5 benchmark functions were tested. Finally, the proposed hybrid ICS and LDM based on KFCM (ICS-LDM-KFCM) was used to identify 4 standard artificial and 6 real world datasets. Compared with other clustering methods, the proposed method obtained more accurate results. This method is verified to be more suitable for complicated datasets with large number of attributes and clusters.

Item Type: Article
Additional Information: Available online 6 January 2016
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 19364
Useful Links:
Depositing User: Xin-She Yang
Date Deposited: 19 Apr 2016 10:58
Last Modified: 10 Jun 2019 13:07
URI: https://eprints.mdx.ac.uk/id/eprint/19364

Actions (login required)

Edit Item Edit Item