Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning
文献类型：期刊论文
| 作者 | Wang, Liang1 ; Geng, Xin3,4; Bezdek, James2; Leckie, Christopher2; Ramamohanarao, Kotagiri2
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| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2010-10-01 |
| 卷号 | 22期号:10页码:1401-1414 |
| 关键词 | Clustering VAT cluster tendency spectral embedding out-of-sample extension |
| 英文摘要 | Visual methods have been widely studied and used in data cluster analysis. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods such as the VAT algorithm generally represent D as an n x n image I((D) over bar) where the objects are reordered to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is their inability to highlight cluster structure when D contains highly complex clusters. This paper addresses this limitation by proposing a Spectral VAT algorithm, where D is mapped to D' in a graph embedding space and then reordered to (D) over tilde using the VAT algorithm. A strategy for automatic determination of the number of clusters in I (D) over tilde' is then proposed, as well as a visual method for cluster formation from I((D) over tilde)' based on the difference between diagonal blocks and off-diagonal blocks. A sampling-based extended scheme is also proposed to enable visual cluster analysis for large data sets. Extensive experimental results on several synthetic and real-world data sets validate our algorithms. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | LARGE DATA SETS ; ALGORITHMS ; VALIDITY ; INDEXES ; NUMBER ; AID |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000281000500005 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9948]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Melbourne, Dept Comp Sci & Software Engn, Melbourne, Vic 3010, Australia 3.Southeast Univ, Sch Engn & Comp Sci, Nanjing 210096, Peoples R China 4.Monash Univ, Sch Math Sci, Clayton, Vic 3800, Australia |
| 推荐引用方式 GB/T 7714 | Wang, Liang,Geng, Xin,Bezdek, James,et al. Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2010,22(10):1401-1414. |
| APA | Wang, Liang,Geng, Xin,Bezdek, James,Leckie, Christopher,&Ramamohanarao, Kotagiri.(2010).Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,22(10),1401-1414. |
| MLA | Wang, Liang,et al."Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 22.10(2010):1401-1414. |
入库方式： OAI收割
来源：自动化研究所
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