Robust On-Line Streaming Clustering

被引：5

作者：

Ibrahim, Omar A. ^{[1
]}

Du, Yizhuo ^{[1
]}

Keller, James ^{[1
]}

机构：

[1] Univ Missouri, Elect Engn & Comp Sci Dept, Columbia, MO 65211 USA

来源：

INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: THEORY AND FOUNDATIONS, IPMU 2018, PT I | 2018年 / 853卷

关键词：

Streaming clustering; Outlier detection; Change detection;

D O I：

10.1007/978-3-319-91473-2_40

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the explosion of ubiquitous continuous sensing, on-line streaming clustering continues to attract attention. The requirements are that the streaming clustering algorithm recognize and adapt clusters as the data evolves, that anomalies are detected, and that new clusters are automatically formed as incoming data dictate. In this paper, we extend an earlier approach, called Extended Robust On-Line Streaming Clustering (EROLSC), which utilizes both the Possibilistic C-Means and Gaussian Mixture Decomposition to perform this task. We show the superiority of EROLSC over traditional streaming clustering algorithms on synthetic and real data sets.

引用

页码：467 / 478

页数：12

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