Clustering algorithm for mixed datasets using density peaks and Self-Organizing Generative Adversarial Networks

Abstract

This paper presents a new Density-Peaks and Self-Organizing Generative Adversarial Networks (DP-SO-GAN) for clustering mixed datasets. Many clustering methods depend on the assumption that datasets contain either categorical or numerical attributes. Nevertheless, in real-time, most of the applications include mixed categorical and numerical attributes. In medicine, the clustering of cardiovascular disease is an essential task. The clustering of such data attributes is a vital and challenging issue. First, we transform mixed data attributes such as categorical attributes using a one-hot encoding technique and numerical attributes using normalization techniques. The converted characteristics are input to a Self-Organizing Generative Adversarial Networks (SO-GAN) to learn the feature map. Second, we train two kernel networks, such as the generator and discriminator, and each one holds a trivial amount of convolution kernels. Last, we propose an enhanced density peaks clustering algorithm and computing similarity measure between the data objects in the feature representation. The clustering accuracy for the cardiovascular disease dataset results in 88.32% with a standard deviation of 0.1 and is relatively higher than that of other existing algorithms. The training time for hand-written digits datasets over 300 epochs is 3148.26 ​s. Experiment results obtained on a set of five datasets demonstrate the merits of the proposed method, especially in terms of the stability and efficiency of network training. The computational complexity of the proposed method in terms of floating-point operations is reduced by around 18% as compared with the classical generative adversarial networks.