G-SteX: Greedy Stem Extension for Free-Length Constrained Motif Discovery

Mohammad, Yasser; Ohmoto, Yoshimasa; Nishida, Toyoaki

doi:10.1007/978-3-642-31087-4_44

Cited by 8 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, we consider each set S i for extension to longer lengths as long as the increase in the length does not increase the variance of point differences between different members of the set in a statistically significant way. This extension is carried out using GSteXS algorithm proposed by the authors in [35]. Due to lack of space, the details of this stage are not elaborated here.…”

Section: Length Extension Stagementioning

confidence: 99%

See 1 more Smart Citation

Unsupervised discovery of basic human actions from activity recording datasets

Mohammad

Nishida

2012

2012 IEEE/SICE International Symposium on System Integration (SII)

Self Cite

View full text Add to dashboard Cite

Human Behavior Understanding (HBU) is a major challenge facing intelligent agents. Most approaches to solve this problem assume a recognition/detection context in which the agent/robot tries to match the perceived behavior to one or more predefined motion patterns (e.g. walking, running etc). A more challenging problem is discovering these motion patterns without apriori assumption about the motions in the data, their duration or their numbers. This paper proposes the utilization of a novel motif discovery algorithm based on the exact MK algorithm to discover basic actions in activity records. The proposed system was evaluated on real records of full body motions and is shown in this paper to achieve high accuracy compared with a recently proposed motif discovery algorithm applied to the same dataset.

show abstract

Section: Length Extension Stagementioning

confidence: 99%

“…Due to lack of space, the details of this stage are not elaborated here. Interesting readers can refer to [35] for more details.…”

Section: Length Extension Stagementioning

confidence: 99%

Unsupervised discovery of basic human actions from activity recording datasets

Mohammad

Nishida

2012

2012 IEEE/SICE International Symposium on System Integration (SII)

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this reason, we consider each set S i for extension to longer lengths as long as the increase in the length does not increase the variance of point differences between different members of the set in a statistically significant way. This extension is carried out using GSteXS algorithm proposed by the authors in [13] which has two alternative extension techniques that will be briefly described in the following paragraphs.…”

Section: Length Extension Stagementioning

confidence: 99%

“…An optional full motif detection can then be run to find all occurrences of each motif in the timeseries [13].…”

Section: Length Extension Stagementioning

confidence: 99%

“…We applied MK++, shift-density based motif discovery (sdCMD) [18], two stem extension algorithms (GSteXS, GSteXB) [23], MCFull [14] and the recently proposed MOEN algorithm [24] with a motif length between 50 and 250 to the concatenated timeseries and calculated the fractions of discovered motifs that cover groundtruth patterns completely, and the fractions that cover partial patterns or multiple patterns. We also calculated the fraction of true patterns that were discovered by every algorithm (covered-fraction) and the extra parts appended to it (extra-motif).…”

Section: Mk++mentioning

confidence: 99%

See 1 more Smart Citation

Exact multi-length scale and mean invariant motif discovery

Mohammad

Nishida

2015

Appl Intell

Self Cite

View full text Add to dashboard Cite

Discovering approximately recurrent motifs (ARMs) in timeseries is an active area of research in data mining. Exact motif discovery is defined as the problem of efficiently finding the most similar pairs of timeseries subsequences and can be used as a basis for discovering ARMs. The most efficient algorithm for solving this problem is the MK algorithm which was designed to find a single pair of timeseries subsequences with maximum similarity at a known length. This paper provides three extensions of the MK algorithm that allow it to find the top K similar subsequences at multiple lengths using both the Euclidean distance metric and scale invariant normalized version of it. The proposed algorithms are then applied to both synthetic data and real-world data with a focus on discovery of ARMs in human motion trajectories.

show abstract