.png){kind=icon}
返回
Projective clustering by histograms
.png){kind=icon}
OA
AI
DOI：10.1109/TKDE.2005.47.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent research suggests that clustering for high-dimensional data should involve searching for hidden subspaces with lower dimensionalities, in which patterns can be observed when data objects are projected onto the subspaces. Discovering such interattribute correlations and location of the corresponding clusters is known as the projective clustering problem. In this paper, we propose an efficient projective clustering technique by histogram construction (EPCH). The histograms help to generate signatures, where a signature corresponds to some region in some subspace, and signatures with a large number of data objects are identified as the regions for subspace clusters. Hence, projected clusters and their corresponding subspaces can be uncovered. Compared to the best previous methods to our knowledge, this approach is more flexible in that less prior knowledge on the data set is required, and it is also much more efficient. Our experiments compare behaviors and performances of this approach and other projective clustering algorithms with different data characteristics. The results show that our technique is scalable to very large databases, and it is able to return accurate clustering results.
Keyword:
projective clustering
histogram
subspace
期刊
IF:
10.4
论文数:
6.9K
被引数:
3.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
PECVD-grown carbon nanotubes on silicon substrates with a nickel-seeded tip-growth structure具有镍种子尖端生长结构的硅衬底上的PECVD生长的碳纳米管
PREAI
分享收藏Diseases of the tooth: the genetic and molecular basis of inherited anomalies affecting the dentition牙齿疾病: 影响牙列的遗传异常的遗传和分子基础
PREAI
分享收藏分享收藏
查看更多内容