SAXually Explicit Images: Finding Unusual Shapes

Li, Wei; Keogh, Eamonn; Xi, Xiaopeng

doi:10.1109/icdm.2006.138

Cited by 78 publications

(48 citation statements)

References 24 publications

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“…Note that once the new breakpoints are defined, and Tables 3 and 4 are appropriately adjusted, the lower bounding property still holds. Finally, in an interesting new development, Wei et al (2006) have shown techniques to adapt SAX to various problems in 2D shape matching, after modifying algorithms/representations to allow for rotation invariance, which in the SAX representation corresponds to circular shifts.…”

Section: Extensions To Saxmentioning

confidence: 99%

Experiencing SAX: a novel symbolic representation of time series

Lin

Keogh

et al. 2007

Data Min Knowl Disc

Self Cite

1,338

851

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Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc. Many researchers have also considered symbolic representations of time series, noting that such representations would potentiality allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities. While many symbolic representations of time series have been introduced over the past decades, they all suffer from two fatal flaws. First, the dimensionality of the symbolic representation is the same as the original data, and virtually all data mining algorithms scale poorly with dimensionality. Second, although distance measures can be defined on the symbolic approaches, these distance measures have little correlation with distance measures defined on the original time series.In this work we formulate a new symbolic representation of time series. Our representation is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measures defined on the original series. As we shall demonstrate, this latter feature is particularly exciting because it allows one to run certain data mining algorithms on the efficiently manipulated symbolic representation, while producing identical results to the algorithms that operate on the original data. In particular, we will demonstrate the utility of our representation on various data mining tasks of clustering, classification, query by content, anomaly detection, motif discovery, and visualization.

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Section: Extensions To Saxmentioning

confidence: 99%

Experiencing SAX: a novel symbolic representation of time series

Lin

Keogh

et al. 2007

Data Min Knowl Disc

Self Cite

1,338

851

View full text Add to dashboard Cite

show abstract

“…Wei et al's rotation-invariant discord discovery technique, which we refer to as RI-DISCORD (Wei et al 2006), is an anomaly detection methods that discovers global anomalies or discords in shape data. Discords, whose definition is reminiscent of the distancebased outlier definition, are the m most anomalous time series (or subregions if the input is a single time series) with the farthest nearest neighbors .…”

Section: Time Series Methods For Unphased Datamentioning

confidence: 99%

Finding anomalous periodic time series

et al. 2008

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Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single continuous time series or a set of time series whose periods are aligned. Light-curve data precludes the use of these methods as the periods of any given pair of light-curves may be out of sync. One may use an existing anomaly detection method if, prior to similarity calculation, one performs the costly act of aligning two light-curves, an operation that scales poorly to massive data sets. This paper presents PCAD, an unsupervised anomaly detection method for large sets of unsynchronized periodic time-series data, that outputs a ranked list of both global and local anomalies. It calculates its anomaly score for each light-curve in relation to a set of centroids produced by a modified k-means clustering algorithm. Our method is able to scale to large data sets through the use of sampling. We validate our method on both light-curve data and other time series data sets. We demonstrate its effectiveness at finding known anomalies, and discuss the effect of sample size and number of centroids on our results. We compare our method to naive solutions and existing time series anomaly detection

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“…There are other versions and extensions of SAX [11], [25], [26], [28]. These versions use it for other applications or apply it to index massive datasets, or compute MINDIST differently [18].…”

Section: The Symbolic Aggregate Approximation (Sax)mentioning

confidence: 99%

Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation

Fuad

2012

Advanced Data Mining and Applications

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Bio-inspired optimization algorithms have been gaining more popularity recently. One of the most important of these algorithms is particle swarm optimization (PSO). PSO is based on the collective intelligence of a swam of particles. Each particle explores a part of the search space looking for the optimal position and adjusts its position according to two factors; the first is its own experience and the second is the collective experience of the whole swarm. PSO has been successfully used to solve many optimization problems. In this work we use PSO to improve the performance of a well-known representation method of time series data which is the symbolic aggregate approximation (SAX). As with other time series representation methods, SAX results in loss of information when applied to represent time series. In this paper we use PSO to propose a new minimum distance WMD for SAX to remedy this problem. Unlike the original minimum distance, the new distance sets different weights to different segments of the time series according to their information content. This weighted minimum distance enhances the performance of SAX as we show through experiments using different time series datasets.

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SAXually Explicit Images: Finding Unusual Shapes

Cited by 78 publications

References 24 publications

Experiencing SAX: a novel symbolic representation of time series

Experiencing SAX: a novel symbolic representation of time series

Finding anomalous periodic time series

Particle Swarm Optimization of Information-Content Weighting of Symbolic Aggregate Approximation

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