caa a22 4500 605478104 CHVBK 20210128100404.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s10994-013-5393-0 doi (NATIONALLICENCE)springer-10.1007/s10994-013-5393-0 Greedy learning of latent tree models for multidimensional clustering [Elektronische Daten] [Teng-Fei Liu, Nevin Zhang, Peixian Chen, April Liu, Leonard Poon, Yi Wang] Real-world data are often multifaceted and can be meaningfully clustered in more than one way. There is a growing interest in obtaining multiple partitions of data. In previous work we learnt from data a latent tree model (LTM) that contains multiple latent variables (Chen et al. 2012). Each latent variable represents a soft partition of data and hence multiple partitions result in. The LTM approach can, through model selection, automatically determine how many partitions there should be, what attributes define each partition, and how many clusters there should be for each partition. It has been shown to yield rich and meaningful clustering results. Our previous algorithm EAST for learning LTMs is only efficient enough to handle data sets with dozens of attributes. This paper proposes an algorithm called BI that can deal with data sets with hundreds of attributes. We empirically compare BI with EAST and other more efficient LTM learning algorithms, and show that BI outperforms its competitors on data sets with hundreds of attributes. In terms of clustering results, BI compares favorably with alternative methods that are not based on LTMs. The Author(s), 2013 Model-based clustering nationallicence Multiple partitions nationallicence Latent tree models nationallicence Liu Teng-Fei Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong aut Zhang Nevin Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong aut Chen Peixian Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong aut Liu April Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong aut Poon Leonard Department of Mathematics and Information Technology, The Hong Kong Institute of Education, Hong Kong, Hong Kong aut Wang Yi Institute of High Performance Computing, A*STAR, 1 Fusionopolis Way, 138632, Singapore, Singapore aut Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 301-330 0885-6125 98:1-2<301 2015 98 10994 https://doi.org/10.1007/s10994-013-5393-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-013-5393-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Teng-Fei Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong aut NATIONALLICENCE 700 1- Zhang Nevin Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong aut NATIONALLICENCE 700 1- Chen Peixian Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong aut NATIONALLICENCE 700 1- Liu April Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong aut NATIONALLICENCE 700 1- Poon Leonard Department of Mathematics and Information Technology, The Hong Kong Institute of Education, Hong Kong, Hong Kong aut NATIONALLICENCE 700 1- Wang Yi Institute of High Performance Computing, A*STAR, 1 Fusionopolis Way, 138632, Singapore, Singapore aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 301-330 0885-6125 98:1-2<301 2015 98 10994