Compression-based data mining of sequential data

Keogh, Eamonn; Lonardi, Stefano; Ratanamahatana, Chotirat Ann; Li, Wei; Lee, Sang‐Hee; Handley, John C.

doi:10.1007/s10618-006-0049-3

Cited by 100 publications

(82 citation statements)

References 36 publications

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“…DTW has been applied to find similarity metrics between signals [26] in particular in the context of gesture recognition, given the potentially more limited number of required samples [33]. More recently, researchers have also developed the derivative DTW (DDTW) [25] and used it to detect activities such as walking, going up and down flights of stairs [39].…”

Section: Dynamic Time Warping (Dtw)mentioning

confidence: 99%

Preprocessing techniques for context recognition from accelerometer data

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et al. 2010

Pers Ubiquit Comput

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The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care, and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers it is possible to determine if a user is walking or running at a specific time of day. By correlating this knowledge with GPS data it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

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Section: Dynamic Time Warping (Dtw)mentioning

confidence: 99%

Preprocessing techniques for context recognition from accelerometer data

Figo

Diniz

Ferreira

et al. 2010

Pers Ubiquit Comput

495

350

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“…Instead, the CK measure works in the spirit of Li and Vitanyi's idea that two objects can be considered similar if information garnered from one can help compress the other (Li et al 2003;Keogh et al 2007). The theoretical implications of this idea have been heavily explored over the last eight years, and numerous applications for discrete data (DNA, natural languages) have emerged.…”

Section: A Review Of the Ck Measurementioning

confidence: 99%

Monitoring and Mining Animal Sounds in Visual Space

2012

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Monitoring animals by the sounds they produce is an important and challenging task, whether the application is outdoors in a natural habitat, or in the controlled environment of a laboratory setting.In the former case, the density and diversity of animal sounds can act as a measure of biodiversity. In the latter case, researchers often create control and treatment groups of animals, expose them to different interventions, and test for different outcomes. One possible manifestation of different outcomes may be changes in the bioacoustics of the animals.With such a plethora of important applications, there have been significant efforts to build bioacoustic classification tools. However, we argue that most current tools are severely limited. They often require the careful tuning of many parameters (and thus huge amounts of training data), are either too computationally expensive for deployment in resource-limited sensors, specialized for a very small group of species, or are simply not accurate enough to be useful.In this work we introduce a novel bioacoustic recognition/classification framework that mitigates or solves all of the above problems. We propose to classify animal sounds in the visual space, by treating the texture of their sonograms as an acoustic fingerprint using a recently introduced parameter-free texture measure as a distance measure. We further show that by searching for the most representative acoustic fingerprint, we can significantly outperform other techniques in terms of speed and accuracy.

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“…NCD was introduced by Cilibrasi and its applicability to various problems such as clustering and classification was demonstrated [8]. NCD has been experimentally evaluated on a number of problems, including: classification of biological sequences and structures [15], novelty detection in patient histories [13], mining of sequential data [23], and even static analysis of source code [3]. It has also been evaluated in terms of the impact of information distortion on the compression [19].…”

Section: Aim and Scopementioning

confidence: 99%

Similarity assessment for removal of noisy end user license agreements

Lavesson

Axelsson

2011

Knowl Inf Syst

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Abstract. In previous work, we have shown the possibility to automatically discriminate between legitimate software and spyware-associated software by performing supervised learning of end user license agreements (EULAs). However, the amount of false positives (spyware classified as legitimate software) was too large for practical use. In this study, the false positives problem is addressed by removing noisy EULAs, which are identified by performing similarity analysis of the previously studied EULAs. Two candidate similarity analysis methods for this purpose are experimentally compared; cosine similarity assessment in conjunction with latent semantic analysis (LSA) and normalized compression distance (NCD). The results show that the number of false positives can be reduced significantly by removing noise identified by either method. However, the experimental results also indicate subtle performance differences between LSA and NCD. To improve the performance even further and to decrease the large number of attributes, the categorical proportional difference (CPD) feature selection algorithm was applied. CPD managed to greatly reduce the number of attributes while at the same time increase classification performance on the original data set, as well as on the LSA-and NCD-based data sets.

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Compression-based data mining of sequential data

Cited by 100 publications

References 36 publications

Preprocessing techniques for context recognition from accelerometer data

Preprocessing techniques for context recognition from accelerometer data

Monitoring and Mining Animal Sounds in Visual Space

Similarity assessment for removal of noisy end user license agreements

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