Unsupervised evolutionary clustering algorithm for mixed type data

Zhi Zheng, Maoguo Gong, Jingjing Ma, Licheng Jiao, Qiaodi Wu

Abstract: In this paper, we propose a novel unsupervised evolutionary clustering algorithm for mixed type data, evolutionary k-prototype algorithm (EKP). As a partitional clustering algorithm, k-prototype (KP) algorithm is a well-known one for mixed type data. However, it is sensitive to initialization and converges to local optimum easily. Global searching ability is one of the most important advantages of evolutionary algorithm (EA), so an EA framework is introduced to help KP overcome its flaws. In this study, KP is applied as a local search strategy, and runs under the control of the EA framework. Experiments on synthetic and real-life datasets show that EKP is more robust and generates much better results than KP for mixed type data.