Unsupervised evolutionary clustering algorithm for mixed type data
作者： Zhi Zheng , Maoguo Gong , Jingjing Ma , Licheng Jiao , Qiaodi Wu
关键词: Algorithm 、 Pattern recognition 、 Artificial intelligence 、 Cluster analysis 、 Canopy clustering algorithm 、 Evolutionary computation 、 Local search (optimization) 、 Computer science 、 Statistical classification 、 Initialization 、 Evolutionary algorithm 、 Unsupervised learning 、 Local optimum 、 Data mining 、 Algorithm design
摘要: In this paper, we propose a novel unsupervised evolutionary clustering algorithm for mixed type data, k-prototype (EKP). As partitional algorithm, (KP) is well-known one data. However, it sensitive to initialization and converges local optimum easily. Global searching ability of the most important advantages (EA), so an EA framework introduced help KP overcome its flaws. study, applied as search strategy, runs under control framework. Experiments on synthetic real-life datasets show that EKP more robust generates much better results than
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