Graded Possibilistic Clustering of Non-stationary Data Streams

被引：2

作者：

Abdullatif, A. ^{[1
,2
]}

Masulli, F. ^{[1
,3
]}

Rovetta, S. ^{[1
]}

Cabri, A. ^{[1
]}

机构：

[1] Univ Genoa, DIBRIS Dept Informat Bioingengering Robot & Syst, Via Dodecaneso 35, I-16146 Genoa, Italy

[2] VEDECOM Inst, Versailles, France

[3] Temple Univ, Coll Sci & Technol, Sbarro Inst Canc Res & Mol Med, Philadelphia, PA 19122 USA

来源：

FUZZY LOGIC AND SOFT COMPUTING APPLICATIONS, WILF 2016 | 2017年 / 10147卷

关键词：

ALGORITHM;

D O I：

10.1007/978-3-319-52962-2_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Multidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier analysis to provide good learning and tracking abilities in both concept shift and concept drift.

引用

页码：139 / 150

页数：12

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