Clustering Over Data Streams Based on Growing Neural Gas

被引：6

作者：

Ghesmoune, Mohammed ^{[1
]}

Lebbah, Mustapha ^{[1
]}

Azzag, Hanene ^{[1
]}

机构：

[1] Univ Paris 13, Sorbonne Paris City LIPN UMR CNRS 7030, F-93430 Villetaneuse, France

来源：

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PART II | 2015年 / 9078卷

关键词：

Data stream clustering; Topological structure; GNG;

D O I：

10.1007/978-3-319-18032-8_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Clustering data streams requires a process capable of partitioning observations continuously with restrictions of memory and time. In this paper we present a new algorithm, called G-Stream, for clustering data streams by making one pass over the data. G-Stream is based on growing neural gas, that allows us to discover clusters of arbitrary shape without any assumptions on the number of clusters. By using a reservoir, and applying a fading function, the quality of clustering is improved. The performance of the proposed algorithm is evaluated on public data sets.

引用

页码：134 / 145

页数：12

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