| Title | Clustering construction on a multimodal probability model |
| Authors | Yu, Jian Yang, Miin-Shen Hao, Pengwei |
| Affiliation | Beijing Jiaotong Univ, Dept Comp Sci, Beijing 100044, Peoples R China. Chung Yuan Christian Univ, Dept Appl Math, Chungli 32023, Taiwan. Peking Univ, Ctr Informat Sci, Beijing 100871, Peoples R China. |
| Keywords | Cluster analysis k-Means Fuzzy c-means Expectation and maximization Multimodal probability model Penalized-type clustering LATENT CLASS MODELS FUZZY C-MEANS MAXIMUM-LIKELIHOOD MEAN SHIFT ALGORITHMS REGRESSION SELECTION CRITERIA |
| Issue Date | 2013 |
| Publisher | information sciences |
| Citation | INFORMATION SCIENCES.2013,237,211-220. |
| Abstract | In 2009, Yu et al. proposed a multimodal probability model (MPM) for clustering. This paper makes advanced clustering constructions on the MPM. We first reconstruct most existing clustering algorithms, such as the k-means, fuzzy c-means, possibilistic c-means, mean shift, classification maximum likelihood, and latent class methods, by establishing the relationships between these clustering algorithms and the MPM. Under our clustering construction, we find that the MPM can be seen as a basic probability model for most existing clustering algorithms. We then construct new clustering frameworks based on the MPM. One of the frameworks develops neW penalized-type clustering algorithms. Another one induces entropy-type clustering algorithms, especially with sample-weighted clustering. Several numerical and real data sets are made for comparisons. These experimental results show that our clustering constructions based on the MPM can produce useful and effective clustering algorithms. (C) 2013 Elsevier Inc. All rights reserved. |
| URI | http://hdl.handle.net/20.500.11897/222064 |
| ISSN | 0020-0255 |
| DOI | 10.1016/j.ins.2013.03.007 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 信息科学技术学院 |
