Automatically assigned DDC number: 00633

Manually assigned DDC number: 006312

Number of references: 30

Title: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases

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Subject: Eamonn Keogh,Kaushik Chakrabarti,Sharad Mehrotra,Michael Pazzani Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases

Description: Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically high dimensionality of the data.. The most promising solutions involve performing dimensionality reduction on the data, then indexing the reduced data with a multidimensional index structure. Many dimensionality reduction techniques have been proposed, including Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and the Discrete Wavelet Transform (DWT). In this work we introduce a new dimensionality reduction technique which we call Adaptive Piecewise Constant Approximation (APCA). While previous techniques (e.g., SVD, DFT and DWT) choose a common representation for all the items in the database that minimizes the global reconstruction error, APCA approximates each time series by a set of constant value segments of varying lengths such that their individual reconstruction errors are minimal. We show how APCA can be ind...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2001-02-07

Pubyear: 2001

Format: ps

Identifier: http://citeseer.ist.psu.edu/419052.html

Source: http://www.ics.uci.edu/~kaushik/research/desc/papers/sigmod01.ps

Language: en

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