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

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.