RECOME: A new density-based clustering algorithm using relative KNN kernel density
文献类型：期刊论文
| 作者 | Geng, Yangli-ao6; Li, Qingyong6; Zheng, Rong5; Zhuang, Fuzhen3,4; He, Ruisi2; Xiong, Naixue1 |
| 刊名 | INFORMATION SCIENCES
 |
| 出版日期 | 2018-04-01 |
| 卷号 | 436页码:13-30 |
| 关键词 | Density-based clustering Density estimation K nearest neighbors Graph theory |
| ISSN号 | 0020-0255 |
| DOI | 10.1016/j.ins.2018.01.013 |
| 英文摘要 | Discovering clusters from a dataset with different shapes, densities, and scales is a known challenging problem in data clustering. In this paper, we propose the RElative COre MErge (RECOME) clustering algorithm. The core of RECOME is a novel density measure, i.e., Relative K nearest Neighbor Kernel Density (RNKD). RECOME identifies core objects with unit RNKD, and partitions non-core objects into atom clusters by successively following higher density neighbor relations toward core objects. Core objects and their corresponding atom clusters are then merged through alpha-reachable paths on a KNN graph. We discover that the number of clusters computed by RECOME is a step function of the a parameter with jump discontinuity on a small collection of values. A fast jump discontinuity discovery (FJDD) method is proposed based on graph theory. RECOME is evaluated on both synthetic datasets and real datasets. Experimental results indicate that RECOME is able to discover clusters with different shapes, densities, and scales. It outperforms six baseline methods on both synthetic datasets and real datasets. Moreover, FJDD is shown to be effective to extract the jump discontinuity set of parameter a for all tested datasets, which can ease the task of data exploration and parameter tuning. (C) 2018 Elsevier Inc. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[61725101] ; National Natural Science Foundation of China[61773361] ; National Natural Science Foundation of China[61771037] ; Beijing Natural Science Foundation[J160004] ; Shanghai Research Program[17511102900] ; National Science and Engineering Council, Canada |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000427311400002 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/5707]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Qingyong |
| 作者单位 | 1.Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK USA 2.Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Key Lab Intelligen Informat Proc, ICT, Beijing 100190, Peoples R China 5.McMaster Univ, Dept Comp & Software, Hamilton, ON, Canada 6.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Geng, Yangli-ao,Li, Qingyong,Zheng, Rong,et al. RECOME: A new density-based clustering algorithm using relative KNN kernel density[J]. INFORMATION SCIENCES,2018,436:13-30. |
| APA | Geng, Yangli-ao,Li, Qingyong,Zheng, Rong,Zhuang, Fuzhen,He, Ruisi,&Xiong, Naixue.(2018).RECOME: A new density-based clustering algorithm using relative KNN kernel density.INFORMATION SCIENCES,436,13-30. |
| MLA | Geng, Yangli-ao,et al."RECOME: A new density-based clustering algorithm using relative KNN kernel density".INFORMATION SCIENCES 436(2018):13-30. |
入库方式： OAI收割
来源：计算技术研究所
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