NDLI: Two Novel Adaptive Symbolic Representations for Similarity Search in Time Series Databases

Content Provider	IEEE Xplore Digital Library
Author	Pham, N.D. Quang Loc Le Tran Khanh Dang
Copyright Year	2010
Abstract	Since the last decade, we have seen an increasing level of interest in time series data mining due to its variety of real-world applications. Numerous representation models of time series have been proposed for data mining, including piecewise polynomial models, spectral models, and the recently proposed symbolic models, such as Symbolic Aggregate approXimation (SAX) and its multiresolution extension, indexable Symbolic Aggregate approXimation (iSAX). In spite of many advantages of dimensionality/numerosity reduction, and lower bounding distance measures, the quality of SAX approximation is highly dependent on the Gaussian distributed property of time series, especially in reduced-dimensionality literature. In this paper, we introduce a novel adaptive symbolic approach based on the combination of SAX and k¬-means algorithm which we call adaptive SAX (aSAX). The proposed representation greatly outperforms the classic SAX not only on the highly Gaussian distribution datasets, but also on the lack of Gaussian distribution datasets with a variety of dimensionality reduction. In addition to being competitive with, or superior to, the classic SAX, we extend aSAX to the multiresolution symbolic representation called indexable adaptive SAX (iaSAX). Our empirical experiments with real-world time series datasets confirm the theoretical analyses as well as the efficiency of the two proposed algorithms in terms of the tightness of lower bound, pruning power and number of random disk accesses.
Starting Page	181
Ending Page	187
File Size	367414
Page Count	7
File Format	PDF
ISBN	9781769540122
e-ISBN	9781424466009
DOI	10.1109/APWeb.2010.23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-06
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Aggregates Piecewise linear approximation Discrete Fourier transforms Chebyshev approximation Gaussian distribution Time measurement Polynomials Discrete wavelet transforms Data mining
Content Type	Text
Resource Type	Article

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