Balanced clustering with least square regression | |
Liu, Hanyang1; Han, Junwei1; Nie, Feiping2; Li, Xuelong3; Nie, Feiping (feipingnie@gmail.com) | |
2017 | |
会议日期 | 2017-02-04 |
会议地点 | San Francisco, CA, United states |
页码 | 2231-2237 |
英文摘要 | Clustering is a fundamental research topic in data mining. A balanced clustering result is often required in a variety of applications. Many existing clustering algorithms have good clustering performances, yet fail in producing balanced clusters. In this paper, we propose a novel and simple method for clustering, referred to as the Balanced Clustering with Least Square regression (BCLS), to minimize the least square linear regression, with a balance constraint to regularize the clustering model. In BCLS, the linear regression is applied to estimate the class-specific hyperplanes that partition each class of data from others, thus guiding the clustering of the data points into different clusters. A balance constraint is utilized to regularize the clustering, by minimizing which can help produce balanced clusters. In addition, we apply the method of augmented Lagrange multipliers (ALM) to help optimize the objective model. The experiments on seven real-world benchmarks demonstrate that our approach not only produces good clustering performance but also guarantees a balanced clustering result. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
产权排序 | 3 |
会议录 | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
会议录出版者 | AAAI press |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/29401] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Nie, Feiping (feipingnie@gmail.com) |
作者单位 | 1.School of Automation, Northwestern Polytechnical University, Xi'an; 710072, China 2.School of Computer Science, Center for OPTIMAL, Northwestern Polytechnical University, Xi'an; 710072, China 3.Center for OPTIMAL, State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China |
推荐引用方式 GB/T 7714 | Liu, Hanyang,Han, Junwei,Nie, Feiping,et al. Balanced clustering with least square regression[C]. 见:. San Francisco, CA, United states. 2017-02-04. |
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