Dimensionality Reduction Techniques for Streaming Time Series: A New Symbolic Approach

被引：4

作者：

Balzanella, Antonio ^{[1
]}

Irpino, Antonio ^{[1
]}

Verde, Rosanna ^{[1
]}

机构：

[1] Univ Naples Federico II, Naples, Italy

来源：

CLASSIFICATION AS A TOOL FOR RESEARCH | 2010年

关键词：

D O I：

10.1007/978-3-642-10745-0_41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A growing number of applications generates massive streams of data which are on-line collected and potentially unbounded in size. To cope with the high dimensionality of data, several strategies for dimensionality reduction have been proposed. In this paper we introduce a new approach to represent an append only data stream into a reduced space. The main aim is to transform a real valued data stream into a string of symbols. The string includes a level component and a shape component allowing to get a better representation of data while maintaining a strong compression ratio.

引用

页码：381 / 389

页数：9

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