.png){kind=icon}
返回
SNCStream+ : Extending a high quality true anytime data stream clustering algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.is.2016.06.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data Stream Clustering is an active area of research which requires efficient algorithms capable of finding and updating clusters incrementally as data arrives. On top of that, due to the inherent evolving nature of data streams, it is expected that algorithms undergo both concept drifts and evolutions, which must be taken into account by the clustering algorithm, allowing incremental clustering updates. In this paper we present the Social Network Clusterer Stream(+) (SNCStream(+)). SNCStream(+) tackles the data stream clustering problem as a network formation and evolution problem, where instances and micro clusters form clusters based on homophily. Our proposal has its parameters analyzed and it is evaluated in a broad set of problems against literature baselines. Results show that SNCStream+ achieves superior clustering quality (CMM), and feasible processing time and memory space usage when compared to the original SNCStream and other proposals of the literature. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
Data stream clustering
Unsupervised learning
Social networks theory
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
2.8K
被引数:
1.8K
机构
引用论文
.png){kind=icon}
Comment on Cain, Ferejohn, and Fiorina (Vol. 78, March 1984, pp. 110–125)评论Cain, Ferejohn, and Fiorina (Vol. 78, March 1984, pp. 110–125)。
PREAI
分享收藏
查看更多内容