Home // DBKDA 2014, The Sixth International Conference on Advances in Databases, Knowledge, and Data Applications // View article

Hierarchical Piecewise Linear Approximation

Authors:
Vineetha Bettaiah
Heggere Ranganath

Keywords: Data Mining; Dimensionality Reduction; Piecewise Linear Representation; Time Series Representation.

Abstract:
This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA achieves dimensionality reduction while preserving prominent local features and general shape of the time series. The HPLA permits coarse-fine processing, allows flexible definition of similarity between two time series based on mathematical measures or general time series shape, and supports query by content, clustering and classification based on whole or subsequence similarity.

Pages: 132 to 138

Copyright: Copyright (c) IARIA, 2014

Publication date: April 20, 2014

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-334-6

Location: Chamonix, France

Dates: from April 20, 2014 to April 24, 2014